Method and system using artificial intelligence to monitor user characteristics during a telemedicine session

ABSTRACT

A computer-implemented system may include a treatment device configured to be manipulated by a user while the user is performing a treatment plan and a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session. The computer-implemented system may also include a first computing device configured to: receive treatment data pertaining to the user while the user uses the treatment device to perform the treatment plan; write to an associated memory, for access by an artificial intelligence engine, the treatment data; receive, from the artificial intelligence engine, at least one prediction; identify a threshold corresponding to the at least one prediction; and, in response to a determination that the at least one prediction is outside of the range of the threshold, update the treatment data pertaining to the user to indicate the at least one prediction.

CROSS-REFERENCES TO RELATED APPLICATIONS

This Continuation-In-Part Patent Application claims priority to and thebenefit of U.S. patent application Ser. No. 17/021,895 filed Sep. 15,2020, titled “Telemedicine for Orthopedic Treatment”, which claimspriority to and the benefit of U.S. Provisional Application Patent Ser.No. 62/910,232 filed Oct. 3, 2019, titled “Telemedicine for OrthopedicTreatment”, and claims priority to and the benefit of U.S. ProvisionalApplication Patent Ser. No. 63/088,657 filed Oct. 7, 2020, titled“Method and System Using Artificial Intelligence to Monitor UserCharacteristics During a Telemedicine Session”, the entire disclosuresof which are hereby incorporated by reference.

BACKGROUND

Remote medical assistance, or telemedicine, may aid a patient inperforming various aspects of a rehabilitation regimen for a body part.The patient may use a patient interface in communication with anassistant interface for receiving the remote medical assistance viaaudio and/or audiovisual and/or other sensorial or perceptive (e.g.,tactile, gustatory, haptic, pressure-sensing-based or electromagnetic(e.g., neurostimulation) communications (e.g., via a computer, asmartphone, or a tablet).

SUMMARY

An aspect of the disclosed embodiments includes a computer-implementedsystem. The computer-implemented system may include a treatment deviceconfigured to be manipulated by a user while the user is performing atreatment plan, and a patient interface comprising an output deviceconfigured to present telemedicine information associated with atelemedicine session. The computer-implemented system may also include afirst computing device configured to: receive treatment data pertainingto the user while the user uses the treatment device to perform thetreatment plan, wherein the treatment data comprises at least one ofcharacteristics of the user, baseline measurement information pertainingto the user, measurement information pertaining to the user while theuser performs the treatment plan, characteristics of the treatmentdevice, and at least one aspect of the treatment plan; write to anassociated memory, for access by an artificial intelligence engine, thetreatment data, the artificial intelligence engine being configured touse at least one machine learning model wherein the machine learningmodel uses at least one aspect of the treatment data to generate atleast one prediction; receive, from the artificial intelligence engine,the at least one prediction; identify a threshold corresponding to theat least one prediction; in response to a determination that the atleast one prediction is within a range of the threshold, provide via aninterface (where such provision includes, without limitation, any formof communication including communication via communicative coupling), ata second computing device of a healthcare provider, the at least oneprediction and the treatment data; and, in response to a determinationthat the at least one prediction is outside of the range of thethreshold, update the treatment data pertaining to the user to indicatethe at least one prediction.

Another aspect of the disclosed embodiments includes a method thatincludes receiving treatment data pertaining to a user who uses atreatment device to perform a treatment plan. The treatment data mayinclude at least one of characteristics of the user, baselinemeasurement information pertaining to the user, measurement informationpertaining to the user while the user performs the treatment plan,characteristics of the treatment device, and at least one aspect of thetreatment plan. The method also may include writing to an associatedmemory, for access by an artificial intelligence engine, the treatmentdata. The artificial intelligence engine may be configured to use atleast one machine learning model that uses at least one aspect of thetreatment data to generate at least one prediction. The method also mayinclude receiving, from the artificial intelligence engine, the at leastone prediction; identifying a threshold corresponding to the at leastone prediction and, in response to a determination that the at least oneprediction is within a range of the threshold, communicating with aninterface, at a computing device of a healthcare provider, the at leastone prediction and the treatment data. The method also may include, inresponse to a determination that the at least one prediction is outsideof the range of the threshold, updating the treatment data pertaining tothe user to indicate the at least one prediction.

Another aspect of the disclosed embodiments includes a system thatincludes a processing device and a memory communicatively coupled to theprocessing device and capable of storing instructions. The processingdevice executes the instructions to perform any of the methods,operations, or steps described herein.

Another aspect of the disclosed embodiments includes a tangible,non-transitory computer-readable medium storing instructions that, whenexecuted, cause a processing device to perform any of the methods,operations, or steps described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is best understood from the following detaileddescription when read in conjunction with the accompanying drawings. Itis emphasized that, according to common practice, the various featuresof the drawings are not to-scale. On the contrary, the dimensions of thevarious features are arbitrarily expanded or reduced for clarity.

FIG. 1 generally illustrates a block diagram of an embodiment of acomputer-implemented system for managing a treatment plan according tothe principles of the present disclosure.

FIG. 2 generally illustrates a perspective view of an embodiment of atreatment device according to the principles of the present disclosure.

FIG. 3 generally illustrates a perspective view of a pedal of thetreatment device of FIG. 2 according to the principles of the presentdisclosure.

FIG. 4 generally illustrates a perspective view of a person using thetreatment device of FIG. 2 according to the principles of the presentdisclosure.

FIG. 5 generally illustrates an example embodiment of an overviewdisplay of an assistant interface according to the principles of thepresent disclosure.

FIG. 6 generally illustrates an example block diagram of training amachine learning model to output, based on data pertaining to thepatient, a treatment plan for the patient according to the principles ofthe present disclosure.

FIG. 7 generally illustrates an embodiment of an overview display of theassistant interface presenting recommended treatment plans and excludedtreatment plans in real-time during a telemedicine session according tothe principles of the present disclosure.

FIG. 8 generally illustrates an embodiment of the overview display ofthe assistant interface presenting, in real-time during a telemedicinesession, recommended treatment plans that have changed as a result ofpatient data changing according to the principles of the presentdisclosure.

FIG. 9 is a flow diagram generally illustrating a method for monitoring,based on treatment data received while a user uses the treatment deviceof FIG. 2, characteristics of the user while the user uses the treatmentdevice according to the principles of the present disclosure.

FIG. 10 is a flow diagram generally illustrating an alternative methodfor monitoring, based on treatment data received while a user uses thetreatment device of FIG. 2, characteristics of the user while the useruses the treatment device according to the principles of the presentdisclosure.

FIG. 11 is a flow diagram generally illustrating an alternative methodfor monitoring, based on treatment data received while a user uses thetreatment device of FIG. 2, characteristics of the user while the useruses the treatment device according to the principles of the presentdisclosure.

FIG. 12 is a flow diagram generally illustrating a method for receivinga selection of an optimal treatment plan and controlling, based on theoptimal treatment plan, a treatment device while the patient uses thetreatment device according to the present disclosure.

FIG. 13 generally illustrates a computer system according to theprinciples of the present disclosure.

NOTATION AND NOMENCLATURE

Various terms are used to refer to particular system components.Different companies may refer to a component by different names—thisdocument does not intend to distinguish between components that differin name but not function. In the following discussion and in the claims,the terms “including” and “comprising” are used in an open-endedfashion, and thus should be interpreted to mean “including, but notlimited to . . . ” Also, the term “couple” or “couples” is intended tomean either an indirect or direct connection. Thus, if a first devicecouples to a second device, that connection may be through a directconnection or through an indirect connection via other devices andconnections.

The terminology used herein is for the purpose of describing particularexample embodiments only, and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. The method steps, processes, and operations described hereinare not to be construed as necessarily requiring their performance inthe particular order discussed or illustrated, unless specificallyidentified as an order of performance. It is also to be understood thatadditional or alternative steps may be employed.

The terms first, second, third, etc. may be used herein to describevarious elements, components, regions, layers and/or sections; however,these elements, components, regions, layers and/or sections should notbe limited by these terms. These terms may be only used to distinguishone element, component, region, layer, or section from another region,layer, or section. Terms such as “first,” “second,” and other numericalterms, when used herein, do not imply a sequence or order unless clearlyindicated by the context. Thus, a first element, component, region,layer, or section discussed below could be termed a second element,component, region, layer, or section without departing from theteachings of the example embodiments. The phrase “at least one of,” whenused with a list of items, means that different combinations of one ormore of the listed items may be used, and only one item in the list maybe needed. For example, “at least one of: A, B, and C” includes any ofthe following combinations: A, B, C, A and B, A and C, B and C, and Aand B and C. In another example, the phrase “one or more” when used witha list of items means there may be one item or any suitable number ofitems exceeding one.

Spatially relative terms, such as “inner,” “outer,” “beneath,” “below,”“lower,” “above,” “upper,” “top,” “bottom,” and the like, may be usedherein. These spatially relative terms can be used for ease ofdescription to describe one element's or feature's relationship toanother element(s) or feature(s) as illustrated in the figures. Thespatially relative terms may also be intended to encompass differentorientations of the device in use, or operation, in addition to theorientation depicted in the figures. For example, if the device in thefigures is turned over, elements described as “below” or “beneath” otherelements or features would then be oriented “above” the other elementsor features. Thus, the example term “below” can encompass both anorientation of above and below. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptions used herein interpreted accordingly.

A “treatment plan” may include one or more treatment protocols, and eachtreatment protocol includes one or more treatment sessions. Eachtreatment session comprises several session periods, with each sessionperiod including a particular exercise for treating the body part of thepatient. For example, a treatment plan for post-operative rehabilitationafter a knee surgery may include an initial treatment protocol withtwice daily stretching sessions for the first 3 days after surgery and amore intensive treatment protocol with active exercise sessionsperformed 4 times per day starting 4 days after surgery. A treatmentplan may also include information pertaining to a medical procedure toperform on the patient, a treatment protocol for the patient using atreatment device, a diet regimen for the patient, a medication regimenfor the patient, a sleep regimen for the patient, additional regimens,or some combination thereof. The treatment plan may also include one ormore training protocols, such as strength training protocols, range ofmotion training protocols, cardiovascular training protocols, endurancetraining protocols, and the like. Each training protocol may include oneor more training sessions comprising several training session periods,with each session period comprising a particular exercise directed toone or more of strength training, range of motion training,cardiovascular training, endurance training, and the like.

The terms telemedicine, telehealth, telemed, teletherapeutic,telemedicine, remote medicine, etc. may be used interchangeably herein.

The term “enhanced reality” may include a user experience comprising oneor more of augmented reality, virtual reality, mixed reality, immersivereality, or a combination of the foregoing (e.g., immersive augmentedreality, mixed augmented reality, virtual and augmented immersivereality, and the like).

The term “augmented reality” may refer, without limitation, to aninteractive user experience that provides an enhanced environment thatcombines elements of a real-world environment with computer-generatedcomponents perceivable by the user.

The term “virtual reality” may refer, without limitation, to a simulatedinteractive user experience that provides an enhanced environmentperceivable by the user and wherein such enhanced environment may besimilar to or different from a real-world environment.

The term “mixed reality” may refer to an interactive user experiencethat combines aspects of augmented reality with aspects of virtualreality to provide a mixed reality environment perceivable by the user.

The term “immersive reality” may refer to a simulated interactive userexperienced using virtual and/or augmented reality images, sounds, andother stimuli to immerse the user, to a specific extent possible (e.g.,partial immersion or total immersion), in the simulated interactiveexperience. For example, in some embodiments, to the specific extentpossible, the user experiences one or more aspects of the immersivereality as naturally as the user typically experiences correspondingaspects of the real-world. Additionally, or alternatively, an immersivereality experience may include actors, a narrative component, a theme(e.g., an entertainment theme or other suitable theme), and/or othersuitable features of components.

The term “body halo” may refer to a hardware component or components,wherein such component or components may include one or more platforms,one or more body supports or cages, one or more chairs or seats, one ormore back supports or back engaging mechanisms, one or more leg or footengaging mechanisms, one or more arm or hand engaging mechanisms, one ormore head engaging mechanisms, other suitable hardware components, or acombination thereof.

As used herein, the term “enhanced environment” may refer to an enhancedenvironment in its entirety, at least one aspect of the enhancedenvironment, more than one aspect of the enhanced environment, or anysuitable number of aspects of the enhanced environment.

As used herein, the term “threshold” and/or the term “range” may includeone or more values expressed as a percentage, an absolute value, a unitof measurement, a difference value, a numerical quantity, or othersuitable expression of the one or more values.

The term “medical action(s)” may refer to any suitable action performedby the medical professional (e.g., or the healthcare professional), andsuch action or actions may include diagnoses, prescription of treatmentplans, prescription of treatment devices, and the making, composingand/or executing of appointments, telemedicine sessions, prescriptionsor medicines, telephone calls, emails, text messages, and the like.

DETAILED DESCRIPTION

The following discussion is directed to various embodiments of thepresent disclosure. Although one or more of these embodiments may bepreferred, the embodiments disclosed should not be interpreted, orotherwise used, as limiting the scope of the disclosure, including theclaims. In addition, one skilled in the art will understand that thefollowing description has broad application, and the discussion of anyembodiment is meant only to be exemplary of that embodiment, and notintended to intimate that the scope of the disclosure, including theclaims, is limited to that embodiment.

Determining optimal remote examination procedures to create an optimaltreatment plan for a patient having certain characteristics (e.g.,vital-sign or other measurements; performance; demographic;psychographic; geographic; diagnostic; measurement- or test-based;medically historic; etiologic; cohort-associative; differentiallydiagnostic; surgical, physically therapeutic, behavioral, pharmacologicand other treatment(s) recommended; etc.) may be a technicallychallenging problem. For example, a multitude of information may beconsidered when determining a treatment plan, which may result ininefficiencies and inaccuracies in the treatment plan selection process.In a rehabilitative setting, some of the multitude of informationconsidered may include characteristics of the patient such as personalinformation, performance information, and measurement information. Thepersonal information may include, e.g., demographic, psychographic orother information, such as an age, a weight, a gender, a height, a bodymass index, a medical condition, a familial medication history, aninjury, a medical procedure, a medication prescribed, behavioral orpsychological conditions, or some combination thereof. The performanceinformation may include, e.g., an elapsed time of using a treatmentdevice, an amount of force exerted on a portion of the treatment device,a range of motion achieved on the treatment device, a movement speed ofa portion of the treatment device, an indication of a plurality of painlevels using the treatment device, or some combination thereof. Themeasurement information may include, e.g., a vital sign, a respirationrate, a heartrate, a temperature, a blood pressure, a glucose level orother biomarker, or some combination thereof. It may be desirable toprocess the characteristics of a multitude of patients, the treatmentplans performed for those patients, and the results of the treatmentplans for those patients.

Further, another technical problem may involve distally treating, via acomputing device during a telemedicine or telehealth session, a patientfrom a location different than a location at which the patient islocated. An additional technical problem is controlling or enabling thecontrol of, from the different location, a treatment device used by thepatient at the location at which the patient is located. Oftentimes,when a patient undergoes rehabilitative surgery (e.g., knee surgery), ahealthcare provider may prescribe a treatment device to the patient touse to perform a treatment protocol at their residence or any mobilelocation or temporary domicile. A healthcare provider may refer to adoctor, physician assistant, nurse, chiropractor, dentist, physicaltherapist, acupuncturist, physical trainer, coach, personal trainer,neurologist, cardiologist, or the like. A healthcare provider may referto any person with a credential, license, degree, or the like in thefield of medicine, physical therapy, rehabilitation, or the like.

When the healthcare provider is located in a different location from thepatient and the treatment device, it may be technically challenging forthe healthcare provider to monitor the patient's actual progress (asopposed to relying on the patient's word about their progress) using thetreatment device, modify the treatment plan according to the patient'sprogress, adapt the treatment device to the personal characteristics ofthe patient as the patient performs the treatment plan, and the like.

Accordingly, systems and methods, such as those described herein,configured to monitor the patient's actual progress, while the patientperforms the treatment plan using the treatment device, may bedesirable. In some embodiments, the systems and methods described hereinmay be configured to receive treatment data pertaining to a user whouses a treatment device to perform a treatment plan. The user mayinclude a patient, user, or person using the treatment device to performvarious exercises.

The treatment data may include various characteristics of the user,various baseline measurement information pertaining to the user, variousmeasurement information pertaining to the user while the user uses thetreatment device, various characteristics of the treatment device, thetreatment plan, other suitable data, or a combination thereof. In someembodiments, the systems and methods described herein may be configuredto receive the treatment data during a telemedicine session.

In some embodiments, while the user uses the treatment device to performthe treatment plan, at least some of the treatment data may correspondto sensor data of a sensor configured to sense various characteristicsof the treatment device, and/or the measurement information of the user.Additionally, or alternatively, while the user uses the treatment deviceto perform the treatment plan, at least some of the treatment data maycorrespond to sensor data from a sensor associated with a wearabledevice configured to sense the measurement information of the user.

The various characteristics of the treatment device may include one ormore settings of the treatment device, a current revolutions per timeperiod (e.g., such as one minute) of a rotating member (e.g., such as awheel) of the treatment device, a resistance setting of the treatmentdevice, other suitable characteristics of the treatment device, or acombination thereof. The baseline measurement information may include,while the user is at rest, one or more vital signs of the user, arespiration rate of the user, a heartrate of the user, a temperature ofthe user, a blood pressure of the user, a glucose level or otherbiomarker, other suitable measurement information of the user, or acombination thereof. The measurement information may include, while theuser uses the treatment device to perform the treatment plan, one ormore vital signs of the user, a respiration rate of the user, aheartrate of the user, a temperature of the user, a blood pressure ofthe user, a glucose level of the user or, other suitable measurementinformation of the user, or a combination thereof.

In some embodiments, the systems and methods described herein may beconfigured to write to an associated memory, for access by an artificialintelligence engine, the treatment data. The artificial intelligenceengine may be configured to use one or more machine learning modelsconfigured to use at least some of the treatment data to generate one ormore predictions. For example, the artificial intelligence engine mayuse a machine learning model trained using various treatment datacorresponding to various users. The machine learning model may beconfigured to receive the treatment data corresponding to the user. Themachine learning model may analyze the at least one aspect of thetreatment data and may generate at least one prediction corresponding tothe at least one aspect of the treatment data. The at least oneprediction may indicate one or more predicted characteristics of theuser. The one or more predicted characteristics of the user may includea predicted vital sign of the user, a predicted respiration rate of theuser, a predicted heartrate of the user, a predicted temperature of theuser, a predicted blood pressure of the user, a predicted performanceparameter of the user performing the treatment plan, a predicted outcomeof the treatment plan being performed by the user, a predicted injury ofthe user resulting from the user performing the treatment plan, or othersuitable predicted characteristics of the user.

In some embodiments, the systems and methods described herein may beconfigured to receive, from the artificial intelligence engine, one ormore predictions. The systems and methods described herein may beconfigured to identify a threshold corresponding to respectivepredictions received from the artificial intelligence engine. Forexample, the systems and methods described herein may identify one ormore characteristics of the user indicated by a respective prediction.

The systems and methods described herein may be configured to access adatabase configured to associate thresholds with characteristics of theuser and/or combinations of characteristics of the user. For example,the database may include information that associates a first thresholdwith a blood pressure of the user. Additionally, or alternatively, thedatabase may include information that associates a threshold with ablood pressure of the user and a heartrate of the user. It should beunderstood that the database may include any number of thresholdsassociated with any of the various characteristics of the user and/orany combination of user characteristics. In some embodiments, athreshold corresponding to a respective prediction may include a valueor a range of values, including an upper limit and a lower limit.

In some embodiments, the systems and methods described herein may beconfigured to determine whether a prediction received from theartificial intelligence engine is within a range of a correspondingthreshold. For example, the systems and methods described herein may beconfigured to compare the prediction to the corresponding threshold. Thesystems and methods described herein may be configured to determinewhether the prediction is within a predefined range of the threshold.For example, if the threshold includes a value, the predefined range mayinclude an upper limit (e.g., 0.5% or 1% percentagewise, or, e.g., 250or 750 (a unit of measurement or other suitable numerical value), orother suitable upper limit) above the value and a lower limit (e.g.,0.5% or 1% percentagewise or, e.g., 250 or 750 (a unit of measurement orother suitable numerical value), or other suitable lower limit) belowthe value. Similarly, if the threshold includes a range including afirst upper limit and a first lower limit (e.g., defining an acceptablerange of the user characteristic or characteristics corresponding to theprediction), the predefined range may include a second upper limit(e.g., 0.5% or 1% percentagewise or, e.g., 250 or 750 (a unit ofmeasurement or other suitable numerical value) or other suitable upperlimit) above the first upper limit and a second lower limit (e.g., 0.5%or 1% percentagewise or, e.g., 250 or 750 (a unit of measurement orother suitable numerical value) or other suitable lower limit) below thefirst lower limit. It should be understood that the threshold mayinclude any suitable predefined range and may include any suitableformat in addition to or other than those described herein.

If the systems and methods described herein determine that theprediction is within the range of the threshold, the systems and methodsdescribed herein may be configured to communicate with (e.g., or over oracross) an interface, at a computing device of a healthcare provider, toprovide the prediction and the treatment data. In some embodiments, thesystems and methods described herein may be configured to generatetreatment information using the treatment data. The treatmentinformation may include a summary of the performance of the treatmentplan by the user while using the treatment device. The summary may beformatted, such that the treatment data is presentable at a computingdevice of the healthcare provider. The systems and methods describedherein may be configured to communicate the treatment information withthe prediction and/or the treatment data, to the computing device of thehealthcare provider. Alternatively, if the systems and methods describedherein determine that the prediction is outside of the range of thethreshold, the systems and methods described herein may be configured toupdate the treatment data pertaining to the user to indicate theprediction.

In some embodiments, the systems and methods described herein may, inresponse to determining that the prediction is within the range of thethreshold, modify at least one aspect of the treatment plan and/or oneor more characteristics of the treatment device based on the prediction.

In some embodiments, the systems and methods described herein may beconfigured to control, while the user uses the treatment device during atelemedicine session and based on a generated prediction, the treatmentdevice. For example, the systems and methods described herein maycontrol one or more characteristics of the treatment device based on theprediction and/or the treatment plan.

The healthcare provider may include a medical professional (e.g., suchas a doctor, a nurse, a therapist, and the like), an exerciseprofessional (e.g., such as a coach, a trainer, a nutritionist, and thelike), or another professional sharing at least one of medical andexercise attributes (e.g., such as an exercise physiologist, a physicaltherapist, an occupational therapist, and the like). As used herein, andwithout limiting the foregoing, a “healthcare provider” may be a humanbeing, a robot, a virtual assistant, a virtual assistant in a virtualand/or augmented reality, or an artificially intelligent entity,including a software program, integrated software and hardware, orhardware alone.

In some embodiments, the interface may include a graphical userinterface configured to provide the treatment information and receiveinput from the healthcare provider. The interface may include one ormore input fields, such as text input fields, dropdown selection inputfields, radio button input fields, virtual switch input fields, virtuallever input fields, audio, haptic, tactile, biometric gesturerecognition, gesture control, touchless user interfaces (TUIs), kineticuser interfaces (KUIs), tangible user interfaces, wired gloves,depth-aware cameras, stereo cameras, gesture-based controllers, orotherwise activated and/or driven input fields, other suitable inputfields, or a combination thereof.

In some embodiments, the healthcare provider may review the treatmentinformation and/or the prediction. The healthcare provider maydetermine, based on the review of the treatment information and/orprediction, whether to modify at least one aspect of the treatment planand/or one or more characteristics of the treatment device. For example,the healthcare provider may review the treatment information. Thehealthcare provider may, based on the review of the treatmentinformation, compare the treatment information to the treatment planbeing performed by the user.

The healthcare provider may compare the following (i) expectedinformation, which pertains to the user while the user uses thetreatment device to perform the treatment plan to (ii) the prediction,which pertains to the user while the user uses the treatment device toperform the treatment plan.

The expected information may include one or more vital signs of theuser, a respiration rate of the user, a heartrate of the user, atemperature of the user, a blood pressure of the user, other suitableinformation of the user, or a combination thereof. The healthcareprovider may determine that the treatment plan is having the desiredeffect if the prediction is within an acceptable range associated withone or more corresponding parts or portions of the expected information.Alternatively, the healthcare provider may determine that the treatmentplan is not having the desired effect if the prediction is outside ofthe range associated with one or more corresponding parts or portions ofthe expected information.

For example, the healthcare provider may determine whether a bloodpressure value indicated by the prediction (e.g., systolic pressure,diastolic pressure, and/or pulse pressure) is within an acceptable range(e.g., plus or minus 1%, plus or minus 5%, percentagewise, plus or minus1 unit of measurement (or other suitable numerical value), or anysuitable percentage-based or numerical range) of an expected bloodpressure value indicated by the expected information. The healthcareprovider may determine that the treatment plan is having the desiredeffect if the blood pressure value is within the range of the expectedblood pressure value. Alternatively, the healthcare provider maydetermine that the treatment plan is not having the desired effect ifthe blood pressure value is outside of the range of the expected bloodpressure value.

In some embodiments, while the user uses the treatment device to performthe treatment plan, the healthcare provider may compare the expectedcharacteristics of the treatment device with characteristics of thetreatment device indicated by the treatment information. For example,the healthcare provider may compare an expected resistance setting ofthe treatment device with an actual resistance setting of the treatmentdevice indicated by the treatment information. The healthcare providermay determine that the user is performing the treatment plan properly ifthe actual characteristics of the treatment device indicated by thetreatment information are within a range of corresponding ones of theexpected characteristics of the treatment device. Alternatively, thehealthcare provider may determine that the user is not performing thetreatment plan properly if the actual characteristics of the treatmentdevice indicated by the treatment information are outside the range ofcorresponding ones of the expected characteristics of the treatmentdevice.

If the healthcare provider determines that the prediction and/or thetreatment information indicates that the user is performing thetreatment plan properly and/or that the treatment plan is having thedesired effect, the healthcare provider may determine not to modify theat least one aspect treatment plan and/or the one or morecharacteristics of the treatment device. Alternatively, while the useruses the treatment device to perform the treatment plan, if thehealthcare provider determines that the prediction and/or the treatmentinformation indicates that the user is not or has not been performingthe treatment plan properly and/or that the treatment plan is not or hasnot been having the desired effect, the healthcare provider maydetermine to modify the at least one aspect of the treatment plan and/orthe one or more characteristics of the treatment device.

In some embodiments, the healthcare provider may interact with theinterface to provide treatment plan input indicating one or moremodifications to the treatment plan and/or to modify one or morecharacteristics of the treatment device, if the healthcare providerdetermines to modify the at least one aspect of the treatment planand/or to modify one or more characteristics of the treatment device.For example, the healthcare provider may use the interface to provideinput indicating an increase or decrease in the resistance setting ofthe treatment device, or other suitable modification to the one or morecharacteristics of the treatment device. Additionally, or alternatively,the healthcare provider may use the interface to provide inputindicating a modification to the treatment plan. For example, thehealthcare provider may use the interface to provide input indicating anincrease or decrease in an amount of time the user is required to usethe treatment device according to the treatment plan, or other suitablemodifications to the treatment plan.

In some embodiments, based on one or more modifications indicated by thetreatment plan input, the systems and methods described herein may beconfigured to modify at least one aspect of the treatment plan and/orone or more characteristics of the treatment device.

In some embodiments, the systems and methods described herein may beconfigured to receive the subsequent treatment data pertaining to theuser while the user uses the treatment device to perform the modifiedtreatment plan. For example, after the healthcare provider providesinput modifying the treatment plan and/or the one or morecharacteristics of the treatment device, and/or after the artificialintelligence engine modifies the treatment plan and/or one or morecharacteristics of the treatment device, the user may continue use thetreatment device to perform the modified treatment plan. The subsequenttreatment data may correspond to treatment data generated while the useruses the treatment device to perform the modified treatment plan. Insome embodiments, the subsequent treatment data may correspond totreatment data generated while the user continues to use the treatmentdevice to perform the treatment plan, after the healthcare provider hasreceived the treatment information and determined not to modify thetreatment plan and/or the one or more characteristics of the treatmentdevice, and/or the artificial intelligence engine has determined not tomodify the treatment plan and/or the one or more characteristics of thetreatment device.

In some embodiments, the artificial intelligence engine may use the oneor more machine learning models to generate one or more subsequentpredictions based on the subsequent treatment data. The systems andmethods described herein may determine whether a respective subsequentprediction is within a range of a corresponding threshold. The systemsand methods described herein may, in response to a determination thatthe respective subsequent prediction is within the range of thethreshold, communicate the subsequent treatment data, subsequenttreatment information, and/or the prediction to the computing device ofthe healthcare provider. In some embodiments, based on the subsequentprediction, the systems and methods described herein may modify at leastone aspect of the treatment plan and/or one or more characteristics ofthe treatment device.

In some embodiments, the systems and methods described herein may beconfigured to receive subsequent treatment plan input from the computingdevice of the healthcare provider. Based on the subsequent treatmentplan input received from the computing device of the healthcareprovider, the systems and methods described herein may be configured tofurther modify the treatment plan and/or to control the one or morecharacteristics of the treatment device. The subsequent treatment planinput may correspond to input provided by the healthcare provider, atthe interface, in response to receiving and/or reviewing subsequenttreatment information and/or the subsequent prediction corresponding tothe subsequent treatment data. It should be understood that the systemsand methods described herein may be configured to continuously and/orperiodically generate predictions based on treatment data. The systemsand methods described herein may be configured to provide treatmentinformation to the computing device of the healthcare provider based ontreatment data continuously and/or periodically received from thesensors or other suitable sources described herein. Additionally, oralternatively, the systems and methods described herein may beconfigured to continuously and/or periodically monitor, while the useruses the treatment device to perform the treatment plan, thecharacteristics of the user.

In some embodiments, the healthcare provider and/or the systems andmethods described herein may receive and/or review, continuously orperiodically, while the user uses the treatment device to perform thetreatment plan, treatment information, treatment data, and orpredictions. Based on one or more trends indicated by the treatmentinformation, treatment data, and/or predictions, the healthcare providerand/or the systems and methods described herein may determine whether tomodify the treatment plan and/or to modify and/or to control the one ormore characteristics of the treatment device. For example, the one ormore trends may indicate an increase in heartrate or other suitabletrends indicating that the user is not performing the treatment planproperly and/or that performance of the treatment plan by the user isnot having the desired effect.

In some embodiments, the systems and methods described herein may beconfigured to use artificial intelligence and/or machine learning toassign patients to cohorts and to dynamically control a treatment devicebased on the assignment during an adaptive telemedicine session. In someembodiments, one or more treatment devices may be provided to patients.The one or more treatment devices may be used by the patients to performtreatment plans in their residences, at a gym, at a rehabilitativecenter, at a hospital, at their work place, at a hotel, at a conferencecenter, or in or at any suitable location, including permanent ortemporary domiciles.

In some embodiments, the treatment devices may be communicativelycoupled to a server. Characteristics of the patients, including thetreatment data, may be collected before, during, and/or after thepatients perform the treatment plans. For example, the personalinformation, the performance information, and the measurementinformation may be collected before, during, and/or after the personperforms the treatment plans. The results (e.g., improved performance ordecreased performance) of performing each exercise may be collected fromthe treatment device throughout the treatment plan and after thetreatment plan is performed. The parameters, settings, configurations,etc. (e.g., position of pedal, amount of resistance, etc.) of thetreatment device may be collected before, during, and/or after thetreatment plan is performed.

Each characteristic of the patient, each result, and each parameter,setting, configuration, etc. may be timestamped and may be correlatedwith a particular step in the treatment plan. Such a technique mayenable determining which steps in the treatment plan are more likely tolead to desired results (e.g., improved muscle strength, range ofmotion, etc.) and which steps are more likely to lead to diminishingreturns (e.g., continuing to exercise after 3 minutes actually delays orharms recovery).

Data may be collected from the treatment devices and/or any suitablecomputing device (e.g., computing devices where personal information isentered, such as the interface of the computing device described herein,a clinician interface, patient interface, and the like) over time as thepatients use the treatment devices to perform the various treatmentplans. The data that may be collected may include the characteristics ofthe patients, the treatment plans performed by the patients, the resultsof the treatment plans, any of the data described herein, any othersuitable data, or a combination thereof.

In some embodiments, the data may be processed to group certain peopleinto cohorts. The people may be grouped by people having certain orselected similar characteristics, treatment plans, and results ofperforming the treatment plans. For example, athletic people having nomedical conditions who perform a treatment plan (e.g., use the treatmentdevice for 30 minutes a day 5 times a week for 3 weeks) and who fullyrecover may be grouped into a first cohort. Older people who areclassified obese and who perform a treatment plan (e.g., use thetreatment plan for 10 minutes a day 3 times a week for 4 weeks) and whoimprove their range of motion by 75 percent may be grouped into a secondcohort.

In some embodiments, an artificial intelligence engine may include oneor more machine learning models that are trained using the cohorts. Forexample, the one or more machine learning models may be trained toreceive an input of characteristics of a new patient and to output atreatment plan for the patient that results in a desired result. Themachine learning models may match a pattern between the characteristicsof the new patient and at least one patient of the patients included ina particular cohort. When a pattern is matched, the machine learningmodels may assign the new patient to the particular cohort and selectthe treatment plan associated with the at least one patient. Theartificial intelligence engine may be configured to control, distallyand based on the treatment plan, the treatment device while the newpatient uses the treatment device to perform the treatment plan.

As may be appreciated, the characteristics of the new patient (e.g., anew user) may change as the new patient uses the treatment device toperform the treatment plan. For example, the performance of the patientmay improve quicker than expected for people in the cohort to which thenew patient is currently assigned. Accordingly, the machine learningmodels may be trained to dynamically reassign, based on the changedcharacteristics, the new patient to a different cohort that includespeople having characteristics similar to the now-changed characteristicsas the new patient. For example, a clinically obese patient may loseweight and no longer meet the weight criterion for the initial cohort,result in the patient's being reassigned to a different cohort with adifferent weight criterion.

A different treatment plan may be selected for the new patient, and thetreatment device may be controlled, distally (e.g., which may bereferred to as remotely) and based on the different treatment plan,while the new patient uses the treatment device to perform the treatmentplan. Such techniques may provide the technical solution of distallycontrolling a treatment device.

Further, the systems and methods described herein may lead to fasterrecovery times and/or better results for the patients because thetreatment plan that most accurately fits their characteristics isselected and implemented, in real-time, at any given moment. “Real-time”may also refer to near real-time, which may be less than 10 seconds. Asdescribed herein, the term “results” may refer to medical results ormedical outcomes. Results and outcomes may refer to responses to medicalactions.

Depending on what result is desired, the artificial intelligence enginemay be trained to output several treatment plans. For example, oneresult may include recovering to a threshold level (e.g., 75% range ofmotion) in a fastest amount of time, while another result may includefully recovering (e.g., 100% range of motion) regardless of the amountof time. The data obtained from the patients and sorted into cohorts mayindicate that a first treatment plan provides the first result forpeople with characteristics similar to the patient's, and that a secondtreatment plan provides the second result for people withcharacteristics similar to the patient.

Further, the artificial intelligence engine may be trained to outputtreatment plans that are not optimal i.e., sub-optimal, nonstandard, orotherwise excluded (all referred to, without limitation, as “excludedtreatment plans”) for the patient. For example, if a patient has highblood pressure, a particular exercise may not be approved or suitablefor the patient as it may put the patient at unnecessary risk or eveninduce a hypertensive crisis and, accordingly, that exercise may beflagged in the excluded treatment plan for the patient. In someembodiments, the artificial intelligence engine may monitor thetreatment data received while the patient (e.g., the user) with, forexample, high blood pressure, uses the treatment device to perform anappropriate treatment plan and may modify the appropriate treatment planto include features of an excluded treatment plan that may providebeneficial results for the patient if the treatment data indicates thepatient is handling the appropriate treatment plan without aggravating,for example, the high blood pressure condition of the patient.

In some embodiments, the treatment plans and/or excluded treatment plansmay be presented, during a telemedicine or telehealth session, to ahealthcare provider. The healthcare provider may select a particulartreatment plan for the patient to cause that treatment plan to betransmitted to the patient and/or to control, based on the treatmentplan, the treatment device. In some embodiments, to facilitatetelehealth or telemedicine applications, including remote diagnoses,determination of treatment plans and rehabilitative and/or pharmacologicprescriptions, the artificial intelligence engine may receive and/oroperate distally from the patient and the treatment device.

In such cases, the recommended treatment plans and/or excluded treatmentplans may be presented simultaneously with a video of the patient inreal-time or near real-time during a telemedicine or telehealth sessionon a user interface of a computing device of a healthcare provider. Thevideo may also be accompanied by audio, text and other multimediainformation. Real-time may refer to less than or equal to 2 seconds.Real-time may also refer to near real-time, which may be less than 10seconds or any reasonably proximate different between two differenttimes. Additionally, or alternatively, near real-time may refer to anyinteraction of a sufficiently short time to enable two individuals toengage in a dialogue via such user interface and will generally be lessthan 10 seconds but greater than 2 seconds.

Presenting the treatment plans generated by the artificial intelligenceengine concurrently with a presentation of the patient video may providean enhanced user interface because the healthcare provider may continueto visually and/or otherwise communicate with the patient while alsoreviewing the treatment plans on the same user interface. The enhanceduser interface may improve the healthcare provider's experience usingthe computing device and may encourage the healthcare provider to reusethe user interface. Such a technique may also reduce computing resources(e.g., processing, memory, network) because the healthcare provider doesnot have to switch to another user interface screen to enter a query fora treatment plan to recommend based on the characteristics of thepatient. The artificial intelligence engine may be configured toprovide, dynamically on the fly, the treatment plans and excludedtreatment plans.

In some embodiments, the treatment device may be adaptive and/orpersonalized because its properties, configurations, and positions maybe adapted to the needs of a particular patient. For example, the pedalsmay be dynamically adjusted on the fly (e.g., via a telemedicine sessionor based on programmed configurations in response to certainmeasurements being detected) to increase or decrease a range of motionto comply with a treatment plan designed for the user. In someembodiments, a healthcare provider may adapt, remotely during atelemedicine session, the treatment device to the needs of the patientby causing a control instruction to be transmitted from a server totreatment device. Such adaptive nature may improve the results ofrecovery for a patient, furthering the goals of personalized medicine,and enabling personalization of the treatment plan on a per-individualbasis.

A technical problem may occur which relates to the informationpertaining to the patient's medical condition being received indisparate formats. For example, a server may receive the informationpertaining to a medical condition of the patient from one or moresources (e.g., from an electronic medical record (EMR) system,application programming interface (API), or any suitable system that hasinformation pertaining to the medical condition of the patient). Thatis, some sources used by various healthcare providers may be installedon their local computing devices and may use proprietary formats.Accordingly, some embodiments of the present disclosure may use an APIto obtain, via interfaces exposed by APIs used by the sources, theformats used by the sources. In some embodiments, when information isreceived from the sources, the API may map, translate and/or convert theformat used by the sources to a standardized format used by theartificial intelligence engine. Further, the information mapped,translated and/or converted to the standardized format used by theartificial intelligence engine may be stored in a database accessed bythe artificial intelligence engine when performing any of the techniquesdisclosed herein. Using the information mapped, translated and/orconverted to a standardized format may enable the more accuratedetermination of the procedures to perform for the patient and/or abilling sequence.

To that end, the standardized information may enable generatingtreatment plans and/or billing sequences having a particular format thatcan be processed by various applications (e.g., telehealth). Forexample, applications, such as telehealth applications, may be executingon various computing devices of medical professionals and/or patients.The applications (e.g., standalone or web-based) may be provided by aserver and may be configured to process data according to a format inwhich the treatment plans and the billing sequences are implemented.Accordingly, the disclosed embodiments may provide a technical solutionby (i) receiving, from various sources (e.g., EMR systems), informationin non-standardized and/or different formats; (ii) standardizing theinformation; and (iii) generating, based on the standardizedinformation, treatment plans and billing sequences having standardizedformats capable of being processed by applications (e.g., telehealthapplication) executing on computing devices of medical professionaland/or patients.

FIG. 1 generally illustrates a block diagram of a computer-implementedsystem 10, hereinafter called “the system” for managing a treatmentplan. Managing the treatment plan may include using an artificialintelligence engine to recommend treatment plans and/or provide excludedtreatment plans that should not be recommended to a patient.

The system 10 also includes a server 30 configured to store (e.g., writeto an associated memory) and to provide data related to managing thetreatment plan. The server 30 may include one or more computers and maytake the form of a distributed and/or virtualized computer or computers.The server 30 also includes a first communication interface 32configured to communicate with (e.g., or over) the clinician interface20 via a first network 34. In some embodiments, the first network 34 mayinclude wired and/or wireless network connections such as Wi-Fi,Bluetooth, ZigBee, Near-Field Communications (NFC), cellular datanetwork, etc. The server 30 includes a first processor 36 and a firstmachine-readable storage memory 38, which may be called a “memory” forshort, holding first instructions 40 for performing the various actionsof the server 30 for execution by the first processor 36.

The server 30 is configured to store data regarding the treatment plan.For example, the memory 38 includes a system data store 42 configured tohold system data, such as data pertaining to treatment plans fortreating one or more patients. The server 30 is also configured to storedata regarding performance by a patient in following a treatment plan.For example, the memory 38 includes a patient data store 44 configuredto hold patient data, such as data pertaining to the one or morepatients, including data representing each patient's performance withinthe treatment plan.

Additionally, or alternatively, the characteristics (e.g., personal,performance, measurement, etc.) of the people, the treatment plansfollowed by the people, the level of compliance with the treatmentplans, and the results of the treatment plans may use correlations andother statistical or probabilistic measures to enable the partitioningof or to partition the treatment plans into different patientcohort-equivalent databases in the patient data store 44. For example,the data for a first cohort of first patients having a first similarinjury, a first similar medical condition, a first similar medicalprocedure performed, a first treatment plan followed by the firstpatient, and a first result of the treatment plan may be stored in afirst patient database. The data for a second cohort of second patientshaving a second similar injury, a second similar medical condition, asecond similar medical procedure performed, a second treatment planfollowed by the second patient, and a second result of the treatmentplan may be stored in a second patient database. Any singlecharacteristic or any combination of characteristics may be used toseparate the cohorts of patients. In some embodiments, the differentcohorts of patients may be stored in different partitions or volumes ofthe same database. There is no specific limit to the number of differentcohorts of patients allowed, other than as limited by mathematicalcombinatoric and/or partition theory.

This characteristic data, treatment plan data, and results data may beobtained from numerous treatment devices and/or computing devices overtime and stored in the database 44. The characteristic data, treatmentplan data, and results data may be correlated in the patient-cohortdatabases in the patient data store 44. The characteristics of thepeople may include personal information, performance information, and/ormeasurement information.

In addition to the historical information about other people stored inthe patient cohort-equivalent databases, real-time or near-real-timeinformation based on the current patient's characteristics about acurrent patient being treated may be stored in an appropriate patientcohort-equivalent database. The characteristics of the patient may bedetermined to match or be similar to the characteristics of anotherperson in a particular cohort (e.g., cohort A) and the patient may beassigned to that cohort.

In some embodiments, the server 30 may execute an artificialintelligence (AI) engine 11 that uses one or more machine learningmodels 13 to perform at least one of the embodiments disclosed herein.The server 30 may include a training engine 9 capable of generating theone or more machine learning models 13. The machine learning models 13may be trained to assign people to certain cohorts based on theircharacteristics, select treatment plans using real-time and historicaldata correlations involving patient cohort-equivalents, and control atreatment device 70, among other things.

The one or more machine learning models 13 may be generated by thetraining engine 9 and may be implemented in computer instructionsexecutable by one or more processing devices of the training engine 9and/or the servers 30. To generate the one or more machine learningmodels 13, the training engine 9 may train the one or more machinelearning models 13. The one or more machine learning models 13 may beused by the artificial intelligence engine 11.

The training engine 9 may be a rackmount server, a router computer, apersonal computer, a portable digital assistant, a smartphone, a laptopcomputer, a tablet computer, a netbook, a desktop computer, an Internetof Things (IoT) device, any other suitable computing device, or acombination thereof. The training engine 9 may be cloud-based or areal-time software platform, and it may include privacy software orprotocols, and/or security software or protocols.

To train the one or more machine learning models 13, the training engine9 may use a training data set of a corpus of the characteristics of thepeople that used the treatment device 70 to perform treatment plans, thedetails (e.g., treatment protocol including exercises, amount of time toperform the exercises, how often to perform the exercises, a schedule ofexercises, parameters/configurations/settings of the treatment device 70throughout each step of the treatment plan, etc.) of the treatment plansperformed by the people using the treatment device 70, and the resultsof the treatment plans performed by the people. The one or more machinelearning models 13 may be trained to match patterns of characteristicsof a patient with characteristics of other people assigned to aparticular cohort. The term “match” may refer to an exact match, acorrelative match, a substantial match, etc. The one or more machinelearning models 13 may be trained to receive the characteristics of apatient as input, map the characteristics to characteristics of peopleassigned to a cohort, and select a treatment plan from that cohort. Theone or more machine learning models 13 may also be trained to control,based on the treatment plan, the machine learning apparatus 70.

Different machine learning models 13 may be trained to recommenddifferent treatment plans for different desired results. For example,one machine learning model may be trained to recommend treatment plansfor most effective recovery, while another machine learning model may betrained to recommend treatment plans based on speed of recovery.

Using training data that includes training inputs and correspondingtarget outputs, the one or more machine learning models 13 may refer tomodel artifacts created by the training engine 9. The training engine 9may find patterns in the training data wherein such patterns map thetraining input to the target output and generate the machine learningmodels 13 that capture these patterns. In some embodiments, theartificial intelligence engine 11, the database 33, and/or the trainingengine 9 may reside on another component (e.g., assistant interface 94,clinician interface 20, etc.) depicted in FIG. 1.

The one or more machine learning models 13 may comprise, e.g., a singlelevel of linear or non-linear operations (e.g., a support vector machine[SVM]) or the machine learning models 13 may be a deep network, i.e., amachine learning model comprising more than one level (e.g., multiplelevels) of non-linear operations. Examples of deep networks are neuralnetworks including generative adversarial networks, convolutional neuralnetworks, recurrent neural networks with one or more hidden layers, andfully connected neural networks (e.g., each neuron may transmit itsoutput signal to the input of the remaining neurons, as well as toitself). For example, the machine learning model may include numerouslayers and/or hidden layers that perform calculations (e.g., dotproducts) using various neurons.

The system 10 also includes a patient interface 50 configured tocommunicate information to a patient and to receive feedback from thepatient. Specifically, the patient interface includes an input device 52and an output device 54, which may be collectively called a patient userinterface 52, 54. The input device 52 may include one or more devices,such as a keyboard, a mouse, a touch screen input, a gesture sensor,and/or a microphone and processor configured for voice recognition. Theoutput device 54 may take one or more different forms including, forexample, a computer monitor or display screen on a tablet, smartphone,or a smart watch. The output device 54 may include other hardware and/orsoftware components such as a projector, virtual reality capability,augmented reality capability, etc. The output device 54 may incorporatevarious different visual, audio, or other presentation technologies. Forexample, the output device 54 may include a non-visual display, such asan audio signal, which may include spoken language and/or other soundssuch as tones, chimes, and/or melodies, which may signal differentconditions and/or directions. The output device 54 may comprise one ormore different display screens presenting various data and/or interfacesor controls for use by the patient. The output device 54 may includegraphics, which may be presented by a web-based interface and/or by acomputer program or application (App.).

As is generally illustrated in FIG. 1, the patient interface 50 includesa second communication interface 56, which may also be called a remotecommunication interface configured to communicate with the server 30and/or the clinician interface 20 via a second network 58. In someembodiments, the second network 58 may include a local area network(LAN), such as an Ethernet network. In some embodiments, the secondnetwork 58 may include the Internet, and communications between thepatient interface 50 and the server 30 and/or the clinician interface 20may be secured via encryption, such as, for example, by using a virtualprivate network (VPN). In some embodiments, the second network 58 mayinclude wired and/or wireless network connections such as Wi-Fi,Bluetooth, ZigBee, Near-Field Communications (NFC), cellular datanetwork, etc. In some embodiments, the second network 58 may be the sameas and/or operationally coupled to the first network 34.

The patient interface 50 includes a second processor 60 and a secondmachine-readable storage memory 62 holding second instructions 64 forexecution by the second processor 60 for performing various actions ofpatient interface 50. The second machine-readable storage memory 62 alsoincludes a local data store 66 configured to hold data, such as datapertaining to a treatment plan and/or patient data, such as datarepresenting a patient's performance within a treatment plan. Thepatient interface 50 also includes a local communication interface 68configured to communicate with various devices for use by the patient inthe vicinity of the patient interface 50. The local communicationinterface 68 may include wired and/or wireless communications. In someembodiments, the local communication interface 68 may include a localwireless network such as Wi-Fi, Bluetooth, ZigBee, Near-FieldCommunications (NFC), cellular data network, etc.

The system 10 also includes a treatment device 70 configured to bemanipulated by the patient and/or to manipulate a body part of thepatient for performing activities according to the treatment plan. Insome embodiments, the treatment device 70 may take the form of anexercise and rehabilitation apparatus configured to perform and/or toaid in the performance of a rehabilitation regimen, which may be anorthopedic rehabilitation regimen, and the treatment includesrehabilitation of a body part of the patient, such as a joint or a boneor a muscle group. The treatment device 70 may be any suitable medical,rehabilitative, therapeutic, etc. apparatus configured to be controlleddistally via another computing device to treat a patient and/or exercisethe patient. The treatment device 70 may be an electromechanical machineincluding one or more weights, an electromechanical bicycle, anelectromechanical spin-wheel, a smart-mirror, a treadmill, or the like.The body part may include, for example, a spine, a hand, a foot, a knee,or a shoulder. The body part may include a part of a joint, a bone, or amuscle group, such as one or more vertebrae, a tendon, or a ligament. Asis generally illustrated in FIG. 1, the treatment device 70 includes acontroller 72, which may include one or more processors, computermemory, and/or other components. The treatment device 70 also includes afourth communication interface 74 configured to communicate with (e.g.or over or across) the patient interface 50 via the local communicationinterface 68. The treatment device 70 also includes one or more internalsensors 76 and an actuator 78, such as a motor. The actuator 78 may beused, for example, for moving the patient's body part and/or forresisting forces by the patient.

The internal sensors 76 may measure one or more operatingcharacteristics of the treatment device 70 such as, for example, a forcea position, a speed, a velocity, and/or an acceleration. In someembodiments, the internal sensors 76 may include a position sensorconfigured to measure at least one of a linear motion or an angularmotion of a body part of the patient. For example, an internal sensor 76in the form of a position sensor may measure a distance that the patientis able to move a part of the treatment device 70, where such distancemay correspond to a range of motion that the patient's body part is ableto achieve. In some embodiments, the internal sensors 76 may include aforce sensor configured to measure a force applied by the patient. Forexample, an internal sensor 76 in the form of a force sensor may measurea force or weight the patient is able to apply, using a particular bodypart, to the treatment device 70.

The system 10 generally illustrated in FIG. 1 also includes anambulation sensor 82, which communicates with the server 30 via thelocal communication interface 68 of the patient interface 50. Theambulation sensor 82 may track and store a number of steps taken by thepatient. In some embodiments, the ambulation sensor 82 may take the formof a wristband, wristwatch, or smart watch. In some embodiments, theambulation sensor 82 may be integrated within a phone, such as asmartphone.

The system 10 generally illustrated in FIG. 1 also includes a goniometer84, which communicates with the server 30 via the local communicationinterface 68 of the patient interface 50. The goniometer 84 measures anangle of the patient's body part. For example, the goniometer 84 maymeasure the angle of flex of a patient's knee or elbow or shoulder.

The system 10 may also include one or more additional sensors (notshown) which communicate with the server 30 via the local communicationinterface 68 of the patient interface 50. The one or more additionalsensors can measure other patient parameters such as a heartrate, atemperature, a blood pressure, a glucose level, the level of anotherbiomarker, one or more vital signs, and the like. For example, the oneor more additional sensors may be optical sensors that detect thereflection of near-infrared light from circulating blood below the levelof the skin. Optical sensors may take the form of a wristband,wristwatch, or smartwatch and measure a glucose level, a heartrate, ablood oxygen saturation level, one or more vital signs, and the like.

In some embodiments, the one or more additional sensors may be locatedin a room or physical space in which the treatment device 70 is beingused, inside the patient's body, disposed on the person's body (e.g.,skin patch), or included in the treatment device 70, and the one or moreadditional sensors may measure various vital signs or otherdiagnostically-relevant attributes (e.g., heartrate, perspiration rate,temperature, blood pressure, oxygen levels, any suitable vital sign,glucose level, a level of another biomarker, etc.). The one or moreadditional sensors may transmit the measurements of the patient to theserver 30 for analysis and processing (e.g., to be used to modify, basedon the measurements, at least the treatment plan for the patient).

The system 10 generally illustrated in FIG. 1 also includes a pressuresensor 86, which communicates with the server 30 via the localcommunication interface 68 of the patient interface 50. The pressuresensor 86 measures an amount of pressure or weight applied by a bodypart of the patient. For example, pressure sensor 86 may measure anamount of force applied by a patient's foot when pedaling a stationarybike.

The system 10 generally illustrated in FIG. 1 also includes asupervisory interface 90 which may be similar or identical to theclinician interface 20. In some embodiments, the supervisory interface90 may have enhanced functionality beyond what is provided on theclinician interface 20. The supervisory interface 90 may be configuredfor use by a person having responsibility for the treatment plan, suchas an orthopedic surgeon.

The system 10 generally illustrated in FIG. 1 also includes a reportinginterface 92 which may be similar or identical to the clinicianinterface 20. In some embodiments, the reporting interface 92 may haveless functionality from what is provided on the clinician interface 20.For example, the reporting interface 92 may not have the ability tomodify a treatment plan. Such a reporting interface 92 may be used, forexample, by a biller to determine the use of the system 10 for billingpurposes. In another example, the reporting interface 92 may not havethe ability to display patient identifiable information, presenting onlypseudonymized data and/or anonymized data for certain data fieldsconcerning a data subject and/or for certain data fields concerning aquasi-identifier of the data subject. Such a reporting interface 92 maybe used, for example, by a researcher to determine various effects of atreatment plan on different patients.

The system 10 includes an assistant interface 94 for a healthcareprovider, such as those described herein, to remotely communicate with(e.g., or over or across) the patient interface 50 and/or the treatmentdevice 70. Such remote communications may enable the healthcare providerto provide assistance or guidance to a patient using the system 10. Morespecifically, the assistant interface 94 is configured to communicate atelemedicine signal 96, 97, 98 a, 98 b, 99 a, 99 b with the patientinterface 50 via a network connection such as, for example, via thefirst network 34 and/or the second network 58. The telemedicine signal96, 97, 98 a, 98 b, 99 a, 99 b comprises one of an audio signal 96, anaudiovisual signal 97, an interface control signal 98 a for controllinga function of the patient interface 50, an interface monitor signal 98 bfor monitoring a status of the patient interface 50, an apparatuscontrol signal 99 a for changing an operating parameter of the treatmentdevice 70, and/or an apparatus monitor signal 99 b for monitoring astatus of the treatment device 70. In some embodiments, each of thecontrol signals 98 a, 99 a may be unidirectional, conveying commandsfrom the assistant interface 94 to the patient interface 50. In someembodiments, in response to successfully receiving a control signal 98a, 99 a and/or to communicate successful and/or unsuccessfulimplementation of the requested control action, an acknowledgementmessage may be sent from the patient interface 50 to the assistantinterface 94. In some embodiments, each of the monitor signals 98 b, 99b may be unidirectional, status-information commands from the patientinterface 50 to the assistant interface 94. In some embodiments, anacknowledgement message may be sent from the assistant interface 94 tothe patient interface 50 in response to successfully receiving one ofthe monitor signals 98 b, 99 b.

In some embodiments, the patient interface 50 may be configured as apass-through for the apparatus control signals 99 a and the apparatusmonitor signals 99 b between the treatment device 70 and one or moreother devices, such as the assistant interface 94 and/or the server 30.For example, the patient interface 50 may be configured to transmit anapparatus control signal 99 a in response to an apparatus control signal99 a within the telemedicine signal 96, 97, 98 a, 98 b, 99 a, 99 b fromthe assistant interface 94.

In some embodiments, the assistant interface 94 may be presented on ashared physical device as the clinician interface 20. For example, theclinician interface 20 may include one or more screens that implementthe assistant interface 94. Alternatively or additionally, the clinicianinterface 20 may include additional hardware components, such as a videocamera, a speaker, and/or a microphone, to implement aspects of theassistant interface 94.

In some embodiments, one or more portions of the telemedicine signal 96,97, 98 a, 98 b, 99 a, 99 b may be generated from a prerecorded source(e.g., an audio recording, a video recording, or an animation) forpresentation by the output device 54 of the patient interface 50. Forexample, a tutorial video may be streamed from the server 30 andpresented upon the patient interface 50. Content from the prerecordedsource may be requested by the patient via the patient interface 50.Alternatively, via a control on the assistant interface 94, thehealthcare provider may cause content from the prerecorded source to beplayed on the patient interface 50.

The assistant interface 94 includes an assistant input device 22 and anassistant display 24, which may be collectively called an assistant userinterface 22, 24. The assistant input device 22 may include one or moreof a telephone, a keyboard, a mouse, a trackpad, or a touch screen, forexample. Alternatively or additionally, the assistant input device 22may include one or more microphones. In some embodiments, the one ormore microphones may take the form of a telephone handset, headset, orwide-area microphone or microphones configured for the healthcareprovider to speak to a patient via the patient interface 50. In someembodiments, assistant input device 22 may be configured to providevoice-based functionalities, with hardware and/or software configured tointerpret spoken instructions by the healthcare provider by using theone or more microphones. The assistant input device 22 may includefunctionality provided by or similar to existing voice-based assistantssuch as Siri by Apple, Alexa by Amazon, Google Assistant, or Bixby bySamsung. The assistant input device 22 may include other hardware and/orsoftware components. The assistant input device 22 may include one ormore general purpose devices and/or special-purpose devices.

The assistant display 24 may take one or more different forms including,for example, a computer monitor or display screen on a tablet, asmartphone, or a smart watch. The assistant display 24 may include otherhardware and/or software components such as projectors, virtual realitycapabilities, or augmented reality capabilities, etc. The assistantdisplay 24 may incorporate various different visual, audio, or otherpresentation technologies. For example, the assistant display 24 mayinclude a non-visual display, such as an audio signal, which may includespoken language and/or other sounds such as tones, chimes, melodies,and/or compositions, which may signal different conditions and/ordirections. The assistant display 24 may comprise one or more differentdisplay screens presenting various data and/or interfaces or controlsfor use by the healthcare provider. The assistant display 24 may includegraphics, which may be presented by a web-based interface and/or by acomputer program or application (App.).

In some embodiments, the system 10 may provide computer translation oflanguage from the assistant interface 94 to the patient interface 50and/or vice-versa. The computer translation of language may includecomputer translation of spoken language and/or computer translation oftext. Additionally or alternatively, the system 10 may provide voicerecognition and/or spoken pronunciation of text. For example, the system10 may convert spoken words to printed text and/or the system 10 mayaudibly speak language from printed text. The system 10 may beconfigured to recognize spoken words by any or all of the patient, theclinician, and/or the healthcare provider. In some embodiments, thesystem 10 may be configured to recognize and react to spoken requests orcommands by the patient. For example, the system 10 may automaticallyinitiate a telemedicine session in response to a verbal command by thepatient (which may be given in any one of several different languages).

In some embodiments, the server 30 may generate aspects of the assistantdisplay 24 for presentation by the assistant interface 94. For example,the server 30 may include a web server configured to generate thedisplay screens for presentation upon the assistant display 24. Forexample, the artificial intelligence engine 11 may generate recommendedtreatment plans and/or excluded treatment plans for patients andgenerate the display screens including those recommended treatment plansand/or external treatment plans for presentation on the assistantdisplay 24 of the assistant interface 94. In some embodiments, theassistant display 24 may be configured to present a virtualized desktophosted by the server 30. In some embodiments, the server 30 may beconfigured to communicate with (e.g., or over) the assistant interface94 via the first network 34. In some embodiments, the first network 34may include a local area network (LAN), such as an Ethernet network.

In some embodiments, the first network 34 may include the Internet, andcommunications between the server 30 and the assistant interface 94 maybe secured via privacy enhancing technologies, such as, for example, byusing encryption over a virtual private network (VPN). Alternatively oradditionally, the server 30 may be configured to communicate with (e.g.,or over or across) the assistant interface 94 via one or more networksindependent of the first network 34 and/or other communication means,such as a direct wired or wireless communication channel. In someembodiments, the patient interface 50 and the treatment device 70 mayeach operate from a patient location geographically separate from alocation of the assistant interface 94. For example, the patientinterface 50 and the treatment device 70 may be used as part of anin-home rehabilitation system, which may be aided remotely by using theassistant interface 94 at a centralized location, such as a clinic or acall center.

In some embodiments, the assistant interface 94 may be one of severaldifferent terminals (e.g., computing devices) that may be groupedtogether, for example, in one or more call centers or at one or moreclinicians' offices. In some embodiments, a plurality of assistantinterfaces 94 may be distributed geographically. In some embodiments, aperson may work as a healthcare provider remotely from any conventionaloffice infrastructure. Such remote work may be performed, for example,where the assistant interface 94 takes the form of a computer and/ortelephone. This remote work functionality may allow for work-from-homearrangements that may include part time and/or flexible work hours for ahealthcare provider.

FIGS. 2-3 show an embodiment of a treatment device 70. Morespecifically, FIG. 2 generally illustrates a treatment device 70 in theform of a stationary cycling machine 100, which may be called astationary bike, for short. The stationary cycling machine 100 includesa set of pedals 102 each attached to a pedal arm 104 for rotation aboutan axle 106. In some embodiments, and as is generally illustrated inFIG. 2, the pedals 102 are movable on the pedal arms 104 in order toadjust a range of motion used by the patient in pedaling. For example,the pedals being located inwardly toward the axle 106 corresponds to asmaller range of motion than when the pedals are located outwardly awayfrom the axle 106. In some embodiments, the pedals may be adjustableinward and outward from the plane of rotation. Such techniques mayenable increasing and decreasing a width of the patient's legs as theypedal. A pressure sensor 86 is attached to or embedded within one of thepedals 102 for measuring an amount of force applied by the patient onthe pedal 102. The pressure sensor 86 may communicate wirelessly to thetreatment device 70 and/or to the patient interface 50.

FIG. 4 generally illustrates a person (a patient) using the treatmentdevice of FIG. 2, and showing sensors and various data parametersconnected to a patient interface 50. The example patient interface 50 isa tablet computer or smartphone, or a phablet, such as an iPad, aniPhone, an Android device, or a Surface tablet, which is held manuallyby the patient. In some other embodiments, the patient interface 50 maybe embedded within or attached to the treatment device 70.

FIG. 4 generally illustrates the patient wearing the ambulation sensor82 on his wrist, with a note showing “STEPS TODAY 1355”, indicating thatthe ambulation sensor 82 has recorded and transmitted that step count tothe patient interface 50. FIG. 4 also generally illustrates the patientwearing the goniometer 84 on his right knee, with a note showing “KNEEANGLE 72°”, indicating that the goniometer 84 is measuring andtransmitting that knee angle to the patient interface 50. FIG. 4 alsogenerally illustrates a right side of one of the pedals 102 with apressure sensor 86 showing “FORCE 12.5 lbs.,” indicating that the rightpedal pressure sensor 86 is measuring and transmitting that forcemeasurement to the patient interface 50.

FIG. 4 also generally illustrates a left side of one of the pedals 102with a pressure sensor 86 showing “FORCE 27 lbs.”, indicating that theleft pedal pressure sensor 86 is measuring and transmitting that forcemeasurement to the patient interface 50. FIG. 4 also generallyillustrates other patient data, such as an indicator of “SESSION TIME0:04:13”, indicating that the patient has been using the treatmentdevice 70 for 4 minutes and 13 seconds. This session time may bedetermined by the patient interface 50 based on information receivedfrom the treatment device 70. FIG. 4 also generally illustrates anindicator showing “PAIN LEVEL 3”. Such a pain level may be obtained fromthe patent in response to a solicitation, such as a question, presentedupon the patient interface 50.

FIG. 5 is an example embodiment of an overview display 120 of theassistant interface 94. Specifically, the overview display 120 presentsseveral different controls and interfaces for the healthcare provider toremotely assist a patient with using the patient interface 50 and/or thetreatment device 70. This remote assistance functionality may also becalled telemedicine or telehealth.

Specifically, the overview display 120 includes a patient profiledisplay 130 presenting biographical information regarding a patientusing the treatment device 70. The patient profile display 130 may takethe form of a portion or region of the overview display 120, as isgenerally illustrated in FIG. 5, although the patient profile display130 may take other forms, such as a separate screen or a popup window.

In some embodiments, the patient profile display 130 may include alimited subset of the patient's biographical information. Morespecifically, the data presented upon the patient profile display 130may depend upon the healthcare provider's need for that information. Forexample, a healthcare provider that is assisting the patient with amedical issue may be provided with medical history information regardingthe patient, whereas a technician troubleshooting an issue with thetreatment device 70 may be provided with a much more limited set ofinformation regarding the patient. The technician, for example, may begiven only the patient's name.

The patient profile display 130 may include pseudonymized data and/oranonymized data or use any privacy enhancing technology to preventconfidential patient data from being communicated in a way that couldviolate patient confidentiality requirements. Such privacy enhancingtechnologies may enable compliance with laws, regulations, or otherrules of governance such as, but not limited to, the Health InsurancePortability and Accountability Act (HIPAA), or the General DataProtection Regulation (GDPR), wherein the patient may be deemed a “datasubject”.

In some embodiments, the patient profile display 130 may presentinformation regarding the treatment plan for the patient to follow inusing the treatment device 70. Such treatment plan information may belimited to a healthcare provider. For example, a healthcare providerassisting the patient with an issue regarding the treatment regimen maybe provided with treatment plan information, whereas a techniciantroubleshooting an issue with the treatment device 70 may not beprovided with any information regarding the patient's treatment plan.

In some embodiments, one or more recommended treatment plans and/orexcluded treatment plans may be presented in the patient profile display130 to the healthcare provider. The one or more recommended treatmentplans and/or excluded treatment plans may be generated by the artificialintelligence engine 11 of the server 30 and received from the server 30in real-time during, inter alia, a telemedicine or telehealth session.An example of presenting the one or more recommended treatment plansand/or ruled-out treatment plans is described below with reference toFIG. 7.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes a patient status display 134 presenting status informationregarding a patient using the treatment device. The patient statusdisplay 134 may take the form of a portion or region of the overviewdisplay 120, as is generally illustrated in FIG. 5, although the patientstatus display 134 may take other forms, such as a separate screen or apopup window.

The patient status display 134 includes sensor data 136 from one or moreof the external sensors 82, 84, 86, and/or from one or more internalsensors 76 of the treatment device 70 and/or one or more additionalsensors (not shown) as has been previously described herein. In someembodiments, the patient status display 134 may include sensor data fromone or more sensors of one or more wearable devices worn by the patientwhile using the treatment device 70. The one or more wearable devicesmay include a watch, a bracelet, a necklace, a chest strap, and thelike. The one or more wearable devices may be configured to monitor aheartrate, a temperature, a blood pressure, a glucose level, a bloodoxygen saturation level, one or more vital signs, and the like of thepatient while the patient is using the treatment device 70. In someembodiments, the patient status display 134 may present other data 138regarding the patient, such as last reported pain level, or progresswithin a treatment plan.

User access controls may be used to limit access, including what data isavailable to be viewed and/or modified, on any or all of the userinterfaces 20, 50, 90, 92, 94 of the system 10. In some embodiments,user access controls may be employed to control what information isavailable to any given person using the system 10. For example, datapresented on the assistant interface 94 may be controlled by user accesscontrols, with permissions set depending on the healthcareprovider/user's need for and/or qualifications to view that information.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes a help data display 140 presenting information for thehealthcare provider to use in assisting the patient. The help datadisplay 140 may take the form of a portion or region of the overviewdisplay 120, as is generally illustrated in FIG. 5. The help datadisplay 140 may take other forms, such as a separate screen or a popupwindow. The help data display 140 may include, for example, presentinganswers to frequently asked questions regarding use of the patientinterface 50 and/or the treatment device 70.

The help data display 140 may also include research data or bestpractices. In some embodiments, the help data display 140 may presentscripts for answers or explanations in response to patient questions. Insome embodiments, the help data display 140 may present flow charts orwalk-throughs for the healthcare provider to use in determining a rootcause and/or solution to a patient's problem.

In some embodiments, the assistant interface 94 may present two or morehelp data displays 140, which may be the same or different, forsimultaneous presentation of help data for use by the healthcareprovider. For example, a first help data display may be used to presenta troubleshooting flowchart to determine the source of a patient'sproblem, and a second help data display may present script informationfor the healthcare provider to read to the patient, such information topreferably include directions for the patient to perform some action,which may help to narrow down or solve the problem. In some embodiments,based upon inputs to the troubleshooting flowchart in the first helpdata display, the second help data display may automatically populatewith script information.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes a patient interface control 150 presenting informationregarding the patient interface 50, and/or to modify one or moresettings of the patient interface 50. The patient interface control 150may take the form of a portion or region of the overview display 120, asis generally illustrated in FIG. 5. The patient interface control 150may take other forms, such as a separate screen or a popup window. Thepatient interface control 150 may present information communicated tothe assistant interface 94 via one or more of the interface monitorsignals 98 b.

As is generally illustrated in FIG. 5, the patient interface control 150includes a display feed 152 of the display presented by the patientinterface 50. In some embodiments, the display feed 152 may include alive copy of the display screen currently being presented to the patientby the patient interface 50. In other words, the display feed 152 maypresent an image of what is presented on a display screen of the patientinterface 50.

In some embodiments, the display feed 152 may include abbreviatedinformation regarding the display screen currently being presented bythe patient interface 50, such as a screen name or a screen number. Thepatient interface control 150 may include a patient interface settingcontrol 154 for the healthcare provider to adjust or to control one ormore settings or aspects of the patient interface 50. In someembodiments, the patient interface setting control 154 may cause theassistant interface 94 to generate and/or to transmit an interfacecontrol signal 98 for controlling a function or a setting of the patientinterface 50.

In some embodiments, the patient interface setting control 154 mayinclude collaborative browsing or co-browsing capability for thehealthcare provider to remotely view and/or to control the patientinterface 50. For example, the patient interface setting control 154 mayenable the healthcare provider to remotely enter text to one or moretext entry fields on the patient interface 50 and/or to remotely controla cursor on the patient interface 50 using a mouse or touchscreen of theassistant interface 94.

In some embodiments, using the patient interface 50, the patientinterface setting control 154 may allow the healthcare provider tochange a setting that cannot be changed by the patient. For example, thepatient interface 50 may be precluded from accessing a language settingto prevent a patient from inadvertently switching, on the patientinterface 50, the language used for the displays, whereas the patientinterface setting control 154 may enable the healthcare provider tochange the language setting of the patient interface 50. In anotherexample, the patient interface 50 may not be able to change a font sizesetting to a smaller size in order to prevent a patient frominadvertently switching the font size used for the displays on thepatient interface 50 such that the display would become illegible to thepatient, whereas the patient interface setting control 154 may providefor the healthcare provider to change the font size setting of thepatient interface 50.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes an interface communications display 156 showing the status ofcommunications between the patient interface 50 and one or more otherdevices 70, 82, 84, such as the treatment device 70, the ambulationsensor 82, and/or the goniometer 84. The interface communicationsdisplay 156 may take the form of a portion or region of the overviewdisplay 120, as is generally illustrated in FIG. 5.

The interface communications display 156 may take other forms, such as aseparate screen or a popup window. The interface communications display156 may include controls for the healthcare provider to remotely modifycommunications with one or more of the other devices 70, 82, 84. Forexample, the healthcare provider may remotely command the patientinterface 50 to reset communications with one of the other devices 70,82, 84, or to establish communications with a new one of the otherdevices 70, 82, 84. This functionality may be used, for example, wherethe patient has a problem with one of the other devices 70, 82, 84, orwhere the patient receives a new or a replacement one of the otherdevices 70, 82, 84.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes an apparatus control 160 for the healthcare provider to viewand/or to control information regarding the treatment device 70. Theapparatus control 160 may take the form of a portion or region of theoverview display 120, as is generally illustrated in FIG. 5. Theapparatus control 160 may take other forms, such as a separate screen ora popup window. The apparatus control 160 may include an apparatusstatus display 162 with information regarding the current status of theapparatus. The apparatus status display 162 may present informationcommunicated to the assistant interface 94 via one or more of theapparatus monitor signals 99 b. The apparatus status display 162 mayindicate whether the treatment device 70 is currently communicating withthe patient interface 50. The apparatus status display 162 may presentother current and/or historical information regarding the status of thetreatment device 70.

The apparatus control 160 may include an apparatus setting control 164for the healthcare provider to adjust or control one or more aspects ofthe treatment device 70. The apparatus setting control 164 may cause theassistant interface 94 to generate and/or to transmit an apparatuscontrol signal 99 (e.g., which may be referred to as treatment planinput, as described) for changing an operating parameter and/or one ormore characteristics of the treatment device 70, (e.g., a pedal radiussetting, a resistance setting, a target RPM, other suitablecharacteristics of the treatment device 70, or a combination thereof).

The apparatus setting control 164 may include a mode button 166 and aposition control 168, which may be used in conjunction for thehealthcare provider to place an actuator 78 of the treatment device 70in a manual mode, after which a setting, such as a position or a speedof the actuator 78, can be changed using the position control 168. Themode button 166 may provide for a setting, such as a position, to betoggled between automatic and manual modes.

In some embodiments, one or more settings may be adjustable at any time,and without having an associated auto/manual mode. In some embodiments,the healthcare provider may change an operating parameter of thetreatment device 70, such as a pedal radius setting, while the patientis actively using the treatment device 70. Such “on the fly” adjustmentmay or may not be available to the patient using the patient interface50.

In some embodiments, the apparatus setting control 164 may allow thehealthcare provider to change a setting that cannot be changed by thepatient using the patient interface 50. For example, the patientinterface 50 may be precluded from changing a preconfigured setting,such as a height or a tilt setting of the treatment device 70, whereasthe apparatus setting control 164 may provide for the healthcareprovider to change the height or tilt setting of the treatment device70.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes a patient communications control 170 for controlling an audioor an audiovisual communications session with the patient interface 50.The communications session with the patient interface 50 may comprise alive feed from the assistant interface 94 for presentation by the outputdevice of the patient interface 50. The live feed may take the form ofan audio feed and/or a video feed. In some embodiments, the patientinterface 50 may be configured to provide two-way audio or audiovisualcommunications with a person using the assistant interface 94.Specifically, the communications session with the patient interface 50may include bidirectional (two-way) video or audiovisual feeds, witheach of the patient interface 50 and the assistant interface 94presenting video of the other one.

In some embodiments, the patient interface 50 may present video from theassistant interface 94, while the assistant interface 94 presents onlyaudio or the assistant interface 94 presents no live audio or visualsignal from the patient interface 50. In some embodiments, the assistantinterface 94 may present video from the patient interface 50, while thepatient interface 50 presents only audio or the patient interface 50presents no live audio or visual signal from the assistant interface 94.

In some embodiments, the audio or an audiovisual communications sessionwith the patient interface 50 may take place, at least in part, whilethe patient is performing the rehabilitation regimen upon the body part.The patient communications control 170 may take the form of a portion orregion of the overview display 120, as is generally illustrated in FIG.5. The patient communications control 170 may take other forms, such asa separate screen or a popup window.

The audio and/or audiovisual communications may be processed and/ordirected by the assistant interface 94 and/or by another device ordevices, such as a telephone system, or a videoconferencing system usedby the healthcare provider while the healthcare provider uses theassistant interface 94. Alternatively or additionally, the audio and/oraudiovisual communications may include communications with a thirdparty. For example, the system 10 may enable the healthcare provider toinitiate a 3-way conversation regarding use of a particular piece ofhardware or software, with the patient and a subject matter expert, suchas a healthcare provider or a specialist. The example patientcommunications control 170 generally illustrated in FIG. 5 includes callcontrols 172 for the healthcare provider to use in managing variousaspects of the audio or audiovisual communications with the patient. Thecall controls 172 include a disconnect button 174 for the healthcareprovider to end the audio or audiovisual communications session. Thecall controls 172 also include a mute button 176 to temporarily silencean audio or audiovisual signal from the assistant interface 94. In someembodiments, the call controls 172 may include other features, such as ahold button (not shown).

The call controls 172 also include one or more record/playback controls178, such as record, play, and pause buttons to control, with thepatient interface 50, recording and/or playback of audio and/or videofrom the teleconference session. The call controls 172 also include avideo feed display 180 for presenting still and/or video images from thepatient interface 50, and a self-video display 182 showing the currentimage of the healthcare provider using the assistant interface 94. Theself-video display 182 may be presented as a picture-in-picture format,within a section of the video feed display 180, as is generallyillustrated in FIG. 5. Alternatively or additionally, the self-videodisplay 182 may be presented separately and/or independently from thevideo feed display 180.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes a third party communications control 190 for use in conductingaudio and/or audiovisual communications with a third party. The thirdparty communications control 190 may take the form of a portion orregion of the overview display 120, as is generally illustrated in FIG.5. The third party communications control 190 may take other forms, suchas a display on a separate screen or a popup window.

The third party communications control 190 may include one or morecontrols, such as a contact list and/or buttons or controls to contact athird party regarding use of a particular piece of hardware or software,e.g., a subject matter expert, such as a healthcare provider or aspecialist. The third party communications control 190 may includeconference calling capability for the third party to simultaneouslycommunicate with both the healthcare provider via the assistantinterface 94, and with the patient via the patient interface 50. Forexample, the system 10 may provide for the healthcare provider toinitiate a 3-way conversation with the patient and the third party.

FIG. 6 generally illustrates an example block diagram of training amachine learning model 13 to output, based on data 600 pertaining to thepatient, a treatment plan 602 for the patient according to the presentdisclosure. Data pertaining to other patients may be received by theserver 30. The other patients may have used various treatment devices toperform treatment plans.

The data may include characteristics of the other patients, the detailsof the treatment plans performed by the other patients, and/or theresults of performing the treatment plans (e.g., a percent of recoveryof a portion of the patients' bodies, an amount of recovery of a portionof the patients' bodies, an amount of increase or decrease in musclestrength of a portion of patients' bodies, an amount of increase ordecrease in range of motion of a portion of patients' bodies, etc.).

As depicted, the data has been assigned to different cohorts. Cohort Aincludes data for patients having similar first characteristics, firsttreatment plans, and first results. Cohort B includes data for patientshaving similar second characteristics, second treatment plans, andsecond results. For example, cohort A may include first characteristicsof patients in their twenties without any medical conditions whounderwent surgery for a broken limb; their treatment plans may include acertain treatment protocol (e.g., use the treatment device 70 for 30minutes 5 times a week for 3 weeks, wherein values for the properties,configurations, and/or settings of the treatment device 70 are set to X(where X is a numerical value) for the first two weeks and to Y (where Yis a numerical value) for the last week).

Cohort A and cohort B may be included in a training dataset used totrain the machine learning model 13. The machine learning model 13 maybe trained to match a pattern between characteristics for each cohortand output the treatment plan that provides the result. Accordingly,when the data 600 for a new patient is input into the trained machinelearning model 13, the trained machine learning model 13 may match thecharacteristics included in the data 600 with characteristics in eithercohort A or cohort B and output the appropriate treatment plan 602. Insome embodiments, the machine learning model 13 may be trained to outputone or more excluded treatment plans that should not be performed by thenew patient.

FIG. 7 generally illustrates an embodiment of an overview display 120 ofthe assistant interface 94 presenting recommended treatment plans andexcluded treatment plans in real-time during a telemedicine sessionaccording to the present disclosure. As depicted, the overview display120 just includes sections for the patient profile 130 and the videofeed display 180, including the self-video display 182. Any suitableconfiguration of controls and interfaces of the overview display 120described with reference to FIG. 5 may be presented in addition to orinstead of the patient profile 130, the video feed display 180, and theself-video display 182.

The healthcare provider using the assistant interface 94 (e.g.,computing device) during the telemedicine session may be presented inthe self-video 182 in a portion of the overview display 120 (e.g., userinterface presented on a display screen 24 of the assistant interface94) that also presents a video from the patient in the video feeddisplay 180. Further, the video feed display 180 may also include agraphical user interface (GUI) object 700 (e.g., a button) that enablesthe healthcare provider to share, in real-time or near real-time duringthe telemedicine session, the recommended treatment plans and/or theexcluded treatment plans with the patient on the patient interface 50.The healthcare provider may select the GUI object 700 to share therecommended treatment plans and/or the excluded treatment plans. Asdepicted, another portion of the overview display 120 includes thepatient profile display 130.

The patient profile display 130 is presenting two example recommendedtreatment plans 600 and one example excluded treatment plan 602. Asdescribed herein, the treatment plans may be recommended in view ofcharacteristics of the patient being treated. To generate therecommended treatment plans 600 the patient should follow to achieve adesired result, a pattern between the characteristics of the patientbeing treated and a cohort of other people who have used the treatmentdevice 70 to perform a treatment plan may be matched by one or moremachine learning models 13 of the artificial intelligence engine 11.Each of the recommended treatment plans may be generated based ondifferent desired results.

For example, as depicted, the patient profile display 130 presents “Thecharacteristics of the patient match characteristics of uses in CohortA. The following treatment plans are recommended for the patient basedon his characteristics and desired results.” Then, the patient profiledisplay 130 presents recommended treatment plans from cohort A, and eachtreatment plan provides different results.

As depicted, treatment plan “A” indicates “Patient X should usetreatment device for 30 minutes a day for 4 days to achieve an increasedrange of motion of Y %; Patient X has Type 2 Diabetes; and Patient Xshould be prescribed medication Z for pain management during thetreatment plan (medication Z is approved for people having Type 2Diabetes).” Accordingly, the treatment plan generated achievesincreasing the range of motion of Y %. As may be appreciated, thetreatment plan also includes a recommended medication (e.g., medicationZ) to prescribe to the patient to manage pain in view of a known medicaldisease (e.g., Type 2 Diabetes) of the patient. That is, the recommendedpatient medication not only does not conflict with the medical conditionof the patient but thereby improves the probability of a superiorpatient outcome. This specific example and all such examples elsewhereherein are not intended to limit in any way the generated treatment planfrom recommending multiple medications, or from handling theacknowledgement, view, diagnosis and/or treatment of comorbid conditionsor diseases.

Recommended treatment plan “B” may specify, based on a different desiredresult of the treatment plan, a different treatment plan including adifferent treatment protocol for a treatment device, a differentmedication regimen, etc.

As depicted, the patient profile display 130 may also present theexcluded treatment plans 602. These types of treatment plans are shownto the healthcare provider using the assistant interface 94 to alert thehealthcare provider not to recommend certain portions of a treatmentplan to the patient. For example, the excluded treatment plan couldspecify the following: “Patient X should not use treatment device forlonger than 30 minutes a day due to a heart condition; Patient X hasType 2 Diabetes; and Patient X should not be prescribed medication M forpain management during the treatment plan (in this scenario, medicationM can cause complications for people having Type 2 Diabetes).Specifically, the excluded treatment plan points out a limitation of atreatment protocol where, due to a heart condition, Patient X should notexercise for more than 30 minutes a day. The ruled-out treatment planalso points out that Patient X should not be prescribed medication Mbecause it conflicts with the medical condition Type 2 Diabetes.

The healthcare provider may select the treatment plan for the patient onthe overview display 120. For example, the healthcare provider may usean input peripheral (e.g., mouse, touchscreen, microphone, keyboard,etc.) to select from the treatment plans 600 for the patient. In someembodiments, during the telemedicine session, the healthcare providermay discuss the pros and cons of the recommended treatment plans 600with the patient.

In any event, the healthcare provider may select the treatment plan forthe patient to follow to achieve the desired result. The selectedtreatment plan may be transmitted to the patient interface 50 forpresentation. The patient may view the selected treatment plan on thepatient interface 50. In some embodiments, the healthcare provider andthe patient may discuss during the telemedicine session the details(e.g., treatment protocol using treatment device 70, diet regimen,medication regimen, etc.) in real-time or in near real-time. In someembodiments, the server 30 may control, based on the selected treatmentplan and during the telemedicine session, the treatment device 70 as theuser uses the treatment device 70.

FIG. 8 generally illustrates an embodiment of the overview display 120of the assistant interface 94 presenting, in real-time during atelemedicine session, recommended treatment plans that have changed as aresult of patient data changing according to the present disclosure. Asmay be appreciated, the treatment device 70 and/or any computing device(e.g., patient interface 50) may transmit data while the patient usesthe treatment device 70 to perform a treatment plan. The data mayinclude updated characteristics of the patient and/or other treatmentdata. For example, the updated characteristics may include newperformance information and/or measurement information. The performanceinformation may include a speed of a portion of the treatment device 70,a range of motion achieved by the patient, a force exerted on a portionof the treatment device 70, a heartrate of the patient, a blood pressureof the patient, a respiratory rate of the patient, and so forth.

In some embodiments, the data received at the server 30 may be inputinto the trained machine learning model 13, which may determine that thecharacteristics indicate the patient is on track for the currenttreatment plan. Determining the patient is on track for the currenttreatment plan may cause the trained machine learning model 13 to adjusta parameter of the treatment device 70. The adjustment may be based on anext step of the treatment plan to further improve the performance ofthe patient.

In some embodiments, the data received at the server 30 may be inputinto the trained machine learning model 13, which may determine that thecharacteristics indicate the patient is not on track (e.g., behindschedule, not able to maintain a speed, not able to achieve a certainrange of motion, is in too much pain, etc.) for the current treatmentplan or is ahead of schedule (e.g., exceeding a certain speed,exercising longer than specified with no pain, exerting more than aspecified force, etc.) for the current treatment plan.

The trained machine learning model 13 may determine that thecharacteristics of the patient no longer match the characteristics ofthe patients in the cohort to which the patient is assigned.Accordingly, the trained machine learning model 13 may reassign thepatient to another cohort that includes qualifying characteristics thepatient's characteristics. As such, the trained machine learning model13 may select a new treatment plan from the new cohort and control,based on the new treatment plan, the treatment device 70.

In some embodiments, prior to controlling the treatment device 70, theserver 30 may provide the new treatment plan 800 to the assistantinterface 94 for presentation in the patient profile 130. As depicted,the patient profile 130 indicates “The characteristics of the patienthave changed and now match characteristics of uses in Cohort B. Thefollowing treatment plan is recommended for the patient based on hischaracteristics and desired results.” Then, the patient profile 130presents the new treatment plan 800 (“Patient X should use the treatmentdevice for 10 minutes a day for 3 days to achieve an increased range ofmotion of L %.” The healthcare provider may select the new treatmentplan 800, and the server 30 may receive the selection. The server 30 maycontrol the treatment device 70 based on the new treatment plan 800. Insome embodiments, the new treatment plan 800 may be transmitted to thepatient interface 50 such that the patient may view the details of thenew treatment plan 800.

In some embodiments, the server 30 may be configured to receivetreatment data pertaining to a user who uses a treatment device 70 toperform a treatment plan. The user may include a patient, user, orperson using the treatment device 70 to perform various exercises.

The treatment data may include various characteristics of the user,various baseline measurement information pertaining to the user, variousmeasurement information pertaining to the user while the user uses thetreatment device 70, various characteristics of the treatment device 70,the treatment plan, other suitable data, or a combination thereof. Insome embodiments, the server 30 may receive the treatment data during atelemedicine session.

In some embodiments, while the user uses the treatment device 70 toperform the treatment plan, at least some of the treatment data mayinclude the sensor data 136 from one or more of the external sensors 82,84, 86, and/or from one or more internal sensors 76 of the treatmentdevice 70. In some embodiments, at least some of the treatment data mayinclude sensor data from one or more sensors of one or more wearabledevices worn by the patient while using the treatment device 70. The oneor more wearable devices may include a watch, a bracelet, a necklace, achest strap, and the like. The one or more wearable devices may beconfigured to monitor a heartrate, a temperature, a blood pressure, oneor more vital signs, and the like of the patient while the patient isusing the treatment device 70.

The various characteristics of the treatment device 70 may include oneor more settings of the treatment device 70, a current revolutions pertime period (e.g., such as one minute) of a rotating member (e.g., suchas a wheel) of the treatment device 70, a resistance setting of thetreatment device 70, other suitable characteristics of the treatmentdevice 70, or a combination thereof. The baseline measurementinformation may include, while the user is at rest, one or more vitalsigns of the user, a respiration rate of the user, a heartrate of theuser, a temperature of the user, a blood pressure of the user, othersuitable measurement information of the user, or a combination thereof.The measurement information may include, while the user uses thetreatment device 70 to perform the treatment plan, one or more vitalsigns of the user, a respiration rate of the user, a heartrate of theuser, a temperature of the user, a blood pressure of the user, othersuitable measurement information of the user, or a combination thereof.

In some embodiments, the server 30 may write to an associated memory,for access by the artificial intelligence engine 11, the treatment data.The artificial intelligence engine 11 may use the one or more machinelearning models 13, which may be configured to use at least some of thetreatment data to generate one or more predictions. For example, theartificial intelligence engine 11 may use a machine learning model 13configured to receive the treatment data corresponding to the user. Themachine learning model 13 may analyze the at least one aspect of thetreatment data and may generate at least one prediction corresponding tothe at least one aspect of the treatment data.

The at least one prediction may indicate one or more predictedcharacteristics of the user. The one or more predicted characteristicsof the user may include a predicted vital sign of the user, a predictedrespiration rate of the user, a predicted heartrate of the user, apredicted temperature of the user, a predicted blood pressure of theuser, a predicted outcome of the treatment plan being performed by theuser, a predicted injury of the user resulting from the user performingthe treatment plan, or other suitable predicted characteristic of theuser.

In some embodiments, the server 30 may receive, from the artificialintelligence engine 11, the one or more predictions. The server 30 mayidentify a threshold corresponding to respective predictions receivedfrom the artificial intelligence engine 11. For example, the server 30may identify one or more characteristics of the user indicated by arespective prediction.

The server 30 may access a database, such as the database 44 or othersuitable database, configured to associate thresholds withcharacteristics of the user and/or combinations of characteristics ofthe user. For example, the database 44 may include information thatassociates a first threshold with a blood pressure of the user.Additionally, or alternatively, the database 44 may include informationthat associates a threshold with a blood pressure of the user and aheartrate of the user. It should be understood that the database 44 mayinclude any number of thresholds associated with any of the variouscharacteristics of the user and/or any combination of usercharacteristics. In some embodiments, a threshold corresponding to arespective prediction may include a value or a range of values includingan upper limit and a lower limit.

In some embodiments, the server 30 may determine whether a predictionreceived from the artificial intelligence engine 11 is within a range ofa corresponding threshold. For example, the server 30 may compare theprediction to the corresponding threshold. The server 30 may determinewhether the prediction is within a predefined range of the threshold.For example, if the threshold includes a value, the predefined range mayinclude an upper limit (e.g., 0.5% or 1% percentagewise or, e.g., 250 or750 (a unit of measurement or other suitable numerical value), or othersuitable upper limit) above the value and a lower limit (e.g., 0.5% or1% percentagewise or, e.g., 250 or 750 (a unit of measurement or othersuitable numerical value), or other suitable lower limit) below thevalue. Similarly, if the threshold includes a range including a firstupper limit and a first lower limit (e.g., defining an acceptable rangeof the user characteristic or characteristics corresponding to theprediction), the predefined range may include a second upper limit(e.g., 0.5% or 1% percentagewise or, e.g., 250 or 750 (a unit ofmeasurement or other suitable numerical value), or other suitable upperlimit) above the first upper limit and a second lower limit (e.g., 0.5%or 1% percentagewise or, e.g., 250 or 750 (a unit of measurement orother suitable numerical value), or other suitable lower limit) belowthe first lower limit. It should be understood that the threshold mayinclude any suitable predefined range and may include any suitableformat in addition to or other than those described herein.

If the server 30 determines that the prediction is within the range ofthe threshold, the server 30 may communicate with (e.g., or over) aninterface, such as the overview display 120 at the computing device ofthe healthcare provider assisting the user, to provide the predictionand the treatment data. In some embodiments, the server 30 may generatetreatment information using the treatment data and/or the prediction.The treatment information may include a formatted summary of theperformance of the treatment plan by the user while using the treatmentdevice 70, such that the treatment data and/or the prediction ispresentable at the computing device of a healthcare provider responsiblefor the performance of the treatment plan by the user. In someembodiments, the patient profile display 120 may include and/or displaythe treatment information.

The server 30 may be configured to provide, at the overview display 120,the treatment information. For example, the server 30 may store thetreatment information for access by the overview display 120 and/orcommunicate the treatment information to the overview display 120. Insome embodiments, the server 30 may provide the treatment information topatient profile display 130 or other suitable section, portion, orcomponent of the overview display 120 or to any other suitable displayor interface.

In some embodiments, the server 30 may, in response to determining thatthe prediction is within the range of the threshold, modify at least oneaspect of the treatment plan and/or, based on the prediction, one ormore characteristics of the treatment device 70. In some embodiments,the server 30 may control, while the user uses the treatment device 70during a telemedicine session and based on a generated prediction, thetreatment device 70. For example, the server 30 may, based on theprediction and/or the treatment plan, control one or morecharacteristics of the treatment device 70.

The healthcare provider may include a medical professional (e.g., suchas a doctor, a nurse, a therapist, and the like), an exerciseprofessional (e.g., such as a coach, a trainer, a nutritionist, and thelike), or another professional sharing at least one of medical andexercise attributes (e.g., such as an exercise physiologist, a physicaltherapist, an occupational therapist, and the like). As used herein, andwithout limiting the foregoing, a “healthcare provider” may be a humanbeing, a robot, a virtual assistant, a virtual assistant in a virtualand/or augmented reality, or an artificially intelligent entity,including a software program, integrated software and hardware, orhardware alone.

In some embodiments, the interface may include a graphical userinterface configured to provide the treatment information and receiveinput from the healthcare provider. The interface may include one ormore input fields, such as text input fields, dropdown selection inputfields, radio button input fields, virtual switch input fields, virtuallever input fields, audio, haptic, tactile, biometric or otherwiseactivated and/or driven input fields, other suitable input fields, or acombination thereof.

In some embodiments, the healthcare provider may review the treatmentinformation and/or the prediction. The healthcare provider may, based onthe review of the treatment information and/or the prediction, determinewhether to modify at least one aspect of the treatment plan and/or oneor more characteristics of the treatment device 70. For example, thehealthcare provider may review the treatment information. The healthcareprovider may, based on the review of the treatment information, comparethe treatment information to the treatment plan being performed by theuser.

The healthcare provider may compare the following: (i) expectedinformation, which pertains to the user while the user uses thetreatment device to perform the treatment plan, to (ii) the prediction,which pertains to the user while the user uses the treatment device toperform the treatment plan.

The expected information may include one or more vital signs of theuser, a respiration rate of the user, a heartrate of the user, atemperature of the user, a blood pressure of the user, other suitableinformation of the user, or a combination thereof. The healthcareprovider may determine that the treatment plan is having the desiredeffect if the prediction is within an acceptable range associated withone or more corresponding parts or portions of the expected information.Alternatively, the healthcare provider may determine that the treatmentplan is not having the desired effect if the prediction is outside ofthe range associated with one or more corresponding parts or portions ofthe expected information.

For example, the healthcare provider may determine whether a bloodpressure value indicated by the prediction (e.g., systolic pressure,diastolic pressure, and/or pulse pressure) is within an acceptable range(e.g., plus or minus 1%, plus or minus 5%, plus or minus 1 unit ofmeasurement (or other suitable numerical value), or any suitable range)of an expected blood pressure value indicated by the expectedinformation. The healthcare provider may determine that the treatmentplan is having the desired effect if the blood pressure value is withinthe range of the expected blood pressure value. Alternatively, thehealthcare provider may determine that the treatment plan is not havingthe desired effect if the blood pressure value is outside of the rangeof the expected blood pressure value.

In some embodiments, while the user uses the treatment device 70 toperform the treatment plan, the healthcare provider may compare theexpected characteristics of the treatment device 70 with characteristicsof the treatment device 70 indicated by the treatment information and/orthe prediction. For example, the healthcare provider may compare anexpected resistance setting of the treatment device 70 with an actualresistance setting of the treatment device 70 indicated by the treatmentinformation and/or the prediction. The healthcare provider may determinethat the user is performing the treatment plan properly if the actualcharacteristics of the treatment device 70 indicated by the treatmentinformation and/or the prediction are within a range of correspondingcharacteristics of the expected characteristics of the treatment device70. Alternatively, the healthcare provider may determine that the useris not performing the treatment plan properly if the actualcharacteristics of the treatment device 70 indicated by the treatmentinformation and/or the prediction are outside the range of correspondingcharacteristics of the expected characteristics of the treatment device70.

If the healthcare provider determines that the prediction and/or thetreatment information indicates that the user is performing thetreatment plan properly and/or that the treatment plan is having thedesired effect, the healthcare provider may determine not to modify theat least one aspect treatment plan and/or the one or morecharacteristics of the treatment device 70. Alternatively, while theuser uses the treatment device 70 to perform the treatment plan, if thehealthcare provider determines that the prediction and/or the treatmentinformation indicates that the user is not or has not been performingthe treatment plan properly and/or that the treatment plan is not or hasnot been having the desired effect, the healthcare provider maydetermine to modify the at least one aspect of the treatment plan and/orthe one or more characteristics of the treatment device 70.

In some embodiments, if the healthcare provider determines to modify theat least one aspect of the treatment plan and/or to modify one or morecharacteristics of the treatment device, the healthcare provider mayinteract with the interface to provide treatment plan input indicatingone or more modifications to the treatment plan and/or to modify one ormore characteristics of the treatment device 70. For example, thehealthcare provider may use the interface to provide input indicating anincrease or decrease in the resistance setting of the treatment device70, or other suitable modification to the one or more characteristics ofthe treatment device 70. Additionally, or alternatively, the healthcareprovider may use the interface to provide input indicating amodification to the treatment plan. For example, the healthcare providermay use the interface to provide input indicating an increase ordecrease in an amount of time the user is required to use the treatmentdevice 70 according to the treatment plan, or other suitablemodifications to the treatment plan.

In some embodiments, based on one or more modifications indicated by thetreatment plan input, the server 30 modify at least one aspect of thetreatment plan and/or one or more characteristics of the treatmentdevice 70 t.

In some embodiments, while the user uses the treatment device 70 toperform the modified treatment plan, the server 30 may receive thesubsequent treatment data pertaining to the user. For example, after thehealthcare provider provides input modifying the treatment plan and/orthe one or more characteristics of the treatment device 70 and/or afterthe server 30, using the artificial intelligence engine 11, modifies thetreatment plan and/or one or more characteristics of the treatmentdevice 70, the user may continue use the treatment device 70 to performthe modified treatment plan. The subsequent treatment data maycorrespond to treatment data generated while the user uses the treatmentdevice 70 to perform the modified treatment plan. In some embodiments,after the healthcare provider has received the treatment information anddetermined not to modify the treatment plan and/or the one or morecharacteristics of the treatment device 70 and/or the server 30, usingthe artificial intelligence engine 11, has determined not to modify thetreatment plan and/or the one or more characteristics of the treatmentdevice 70, the subsequent treatment data may correspond to treatmentdata generated while the user continues to use the treatment device 70to perform the treatment plan. In some embodiments, the subsequenttreatment data may include the updated treatment data (e.g., thetreatment data updated to include the at least one prediction),

In some embodiments, the server 30 may use the artificial intelligenceengine 11 using the machine learning model 13 to generate one or moresubsequent predictions based on the subsequent treatment data. Theserver 30 may determine whether a respective subsequent prediction iswithin a range of a corresponding threshold. The server 30 may, inresponse to a determination that the respective subsequent prediction iswithin the range of the threshold, communicate the subsequent treatmentdata, subsequent treatment information, and/or the prediction to thecomputing device of the healthcare provider. In some embodiments, theserver 30 may modify at least one aspect of the treatment plan and/orone or more characteristics of the treatment device 70 based on thesubsequent prediction.

In some embodiments, the server 30 may receive subsequent treatment planinput from the computing device of the healthcare provider. Based on thesubsequent treatment plan input received from the computing device ofthe healthcare provider, the server 30 may further modify the treatmentplan and/or control the one or more characteristics of the treatmentdevice 70. The subsequent treatment plan input may correspond to inputprovided by the healthcare provider, at the interface, in response toreceiving and/or reviewing subsequent treatment information and/or thesubsequent prediction corresponding to the subsequent treatment data. Itshould be understood that the server 30, using the artificialintelligence engine 11, may continuously and/or periodically generatepredictions based on treatment data. Based on treatment datacontinuously and/or periodically received from the sensors or othersuitable sources described herein, the server 30 may provide treatmentinformation and/or predictions to the computing device of the healthcareprovider. Additionally, or alternatively, the server 30 may continuouslyand/or periodically monitor, while the user uses the treatment device 70to perform the treatment plan, the characteristics of the user.

In some embodiments, the healthcare provider and/or the server 30 mayreceive and/or review, continuously or periodically, while the user usesthe treatment device 70 to perform the treatment plan, treatmentinformation, treatment data, and or predictions. Based on one or moretrends indicated by the treatment information, treatment data, and/orpredictions, the healthcare provider and/or the server 30 may determinewhether to modify the treatment plan and/or to modify and/or to controlthe one or more characteristics of the treatment device 70. For example,the one or more trends may indicate an increase in heartrate or othersuitable trends indicating that the user is not performing the treatmentplan properly and/or that performance of the treatment plan by the useris not having the desired effect.

In some embodiments, the server 30 may control, while the user uses thetreatment device 70 to perform the treatment plan, one or morecharacteristics of the treatment device 70 based on the prediction. Forexample, the server 30 may determine that the prediction is outside ofthe range of the corresponding threshold. Based on the prediction, theserver 30 may identify one or more characteristics of the treatmentdevice 70. The server 30 may communicate a signal to the controller 72of the treatment device 70 indicating the modifications to the one ormore characteristics of the treatment device 70. Based on the signal,the controller 72 may modify the one or more characteristics of thetreatment device 70.

In some embodiments, the treatment plan, including the configurations,settings, range of motion settings, pain level, force settings, andspeed settings, etc. of the treatment device 70 for various exercises,may be transmitted to the controller of the treatment device 70. In oneexample, if the user provides an indication, via the patient interface50, that he is experiencing a high level of pain at a particular rangeof motion, the controller may receive the indication. Based on theindication, the controller may electronically adjust the range of motionof the pedal 102 by adjusting the pedal inwardly, outwardly, or along orabout any suitable axis, via one or more actuators, hydraulics, springs,electric motors, or the like. When the user indicates certain painlevels during an exercise, the treatment plan may define alternativerange of motion settings for the pedal 102. Accordingly, once thetreatment plan is uploaded to the controller of the treatment device 70,the treatment device 70 may continue to operate without furtherinstruction, further external input, and the like. It should be notedthat the patient (via the patient interface 50) and/or the assistant(via the assistant interface 94) may override any of the configurationsor settings of the treatment device 70 at any time. For example, thepatient may use the patient interface 50 to cause the treatment device70 to immediately stop, if so desired.

FIG. 9 is a flow diagram generally illustrating a method 900 formonitoring, based on treatment data received while a user uses thetreatment device 70, characteristics of the user while the user uses thetreatment device 70 according to the principles of the presentdisclosure. The method 900 is performed by processing logic that mayinclude hardware (circuitry, dedicated logic, etc.), software (such asis run on a general-purpose computer system or a dedicated machine), ora combination of both. The method 900 and/or each of its individualfunctions, routines, subroutines, or operations may be performed by oneor more processors of a computing device (e.g., any component of FIG. 1,such as server 30 executing the artificial intelligence engine 11). Insome embodiments, the method 900 may be performed by a single processingthread. Alternatively, the method 900 may be performed by two or moreprocessing threads, each thread implementing one or more individualfunctions, routines, subroutines, or operations of the methods.

For simplicity of explanation, the method 900 is depicted and describedas a series of operations. However, operations in accordance with thisdisclosure can occur in various orders and/or concurrently, and/or withother operations not presented and described herein. For example, theoperations depicted in the method 900 may occur in combination with anyother operation of any other method disclosed herein. Furthermore, notall illustrated operations may be required to implement the method 900in accordance with the disclosed subject matter. In addition, thoseskilled in the art will understand and appreciate that the method 900could alternatively be represented as a series of interrelated statesvia a state diagram or events.

At 902, the processing device may receive treatment data pertaining to auser who uses a treatment device, such as the treatment device 70, toperform a treatment plan. The treatment data may include characteristicsof the user, baseline measurement information pertaining to the user,measurement information pertaining to the user while the user uses thetreatment device 70 to perform the treatment plan, characteristics ofthe treatment device 70, at least one aspect of the treatment plan,other suitable data, or a combination thereof.

At 904, the processing device may write to an associated memory, foraccess by an artificial intelligence engine, such as the artificialintelligence engine 11, the treatment data. The artificial intelligenceengine 11 may be configured to use at least one machine learning model,such as the machine learning model 13. The machine learning model 13 maybe configured to use at least one aspect of the treatment data togenerate at least one prediction.

The at least one prediction may indicate one or more predictedcharacteristics of the user. The one or more predicted characteristicsof the user may include a predicted vital sign of the user, a predictedrespiration rate of the user, a predicted heartrate of the user, apredicted temperature of the user, a predicted blood pressure of theuser, a predicted outcome of the treatment plan being performed by theuser, a predicted injury of the user resulting from the user performingthe treatment plan, or other suitable predicted characteristic of theuser.

At 906, the processing device may receive, from the artificialintelligence engine 11, the at least one prediction.

At 908, the processing device may identify a threshold corresponding tothe at least one prediction. For example, the processing device mayidentify one or more characteristics of the user indicated by arespective prediction. The processing device may access a database, suchas the database 44 or other suitable database, configured to associatethresholds with characteristics of the user and/or combinations ofcharacteristics of the user. For example, the database 44 may includeinformation that associates a first threshold with a blood pressure ofthe user. Additionally, or alternatively, the database 44 may includeinformation that associates a threshold with a blood pressure of theuser and a heartrate of the user. A threshold corresponding to arespective prediction may include a value or a range of values includingan upper limit and a lower limit.

At 910, the processing device may, in response to a determination thatthe at least one prediction is within a range of a correspondingthreshold, communicate with an interface at a computing device of ahealthcare provider, to provide the at least one prediction and thetreatment data. For example, the processing device may compare the atleast one prediction and/or one or more characteristic of the userindicated by the prediction with the corresponding threshold identifiedby the processing device. If the processing device determines that theprediction is within the range of the threshold, the processing devicemay communicate the at least one prediction and/or the treatment data tothe computing device of the healthcare provider.

At 912, the processing device may, in response to a determination thatthe at least one prediction is outside of the range of the correspondingthreshold, update the treatment data pertaining to the user to indicatethe at least one prediction. The processing device may store the updatedtreatment data in an associated memory.

FIG. 10 is a flow diagram generally illustrating an alternative method1000 for monitoring, based on treatment data received while a user usesthe treatment device 70, characteristics of the user while the user usesthe treatment device 70 according to the principles of the presentdisclosure. Method 1000 includes operations performed by processors of acomputing device (e.g., any component of FIG. 1, such as server 30executing the artificial intelligence engine 11). In some embodiments,one or more operations of the method 1000 are implemented in computerinstructions stored on a memory device and executed by a processingdevice. The method 1000 may be performed in the same or a similar manneras described above in regard to method 900. The operations of the method1000 may be performed in some combination with any of the operations ofany of the methods described herein.

At 1002, the processing device may receive treatment data, during atelemedicine session, pertaining to a user who uses a treatment device,such as the treatment device 70, to perform a treatment plan. Thetreatment data may include characteristics of the user, baselinemeasurement information pertaining to the user, measurement informationpertaining to the user while the user uses the treatment device 70 toperform the treatment plan, characteristics of the treatment device 70,at least one aspect of the treatment plan, other suitable data, or acombination thereof.

At 1004, the processing device may write to an associated memory, foraccess by an artificial intelligence engine, such as the artificialintelligence engine 11, the treatment data. The artificial intelligenceengine 11 may be configured to use at least one machine learning model,such as the machine learning model 13. The machine learning model 13 maybe configured to use at least one aspect of the treatment data togenerate at least one prediction.

The at least one prediction may indicate one or more predictedcharacteristics of the user. The one or more predicted characteristicsof the user may include a predicted vital sign of the user, a predictedrespiration rate of the user, a predicted heartrate of the user, apredicted temperature of the user, a predicted blood pressure of theuser, a predicted outcome of the treatment plan being performed by theuser, a predicted injury of the user resulting from the user performingthe treatment plan, or other suitable predicted characteristic of theuser.

At 1006, the processing device may receive from the artificialintelligence engine 11 the at least one prediction.

At 1008, the processing device may identify a threshold corresponding tothe at least one prediction. For example, the processing device mayidentify one or more characteristics of the user indicated by arespective prediction. The processing device may access a database, suchas the database 44 or other suitable database, configured to associatethresholds with characteristics of the user and/or combinations ofcharacteristics of the user. For example, the database 44 may includeinformation that associates a first threshold with a blood pressure ofthe user. Additionally, or alternatively, the database 44 may includeinformation that associates a threshold with a blood pressure of theuser and a heartrate of the user. A threshold corresponding to arespective prediction may include a value or a range of values includingan upper limit and a lower limit.

At 1010, the processing device may, in response to a determination thatthe at least one prediction is within a range of a correspondingthreshold, communicate with an interface at a computing device of ahealthcare provider, to provide the at least one prediction and thetreatment data. For example, the processing device may compare the atleast one prediction and/or one or more characteristic of the userindicated by the prediction with the corresponding threshold identifiedby the processing device. If the processing device determines that theprediction is within the range of the threshold, the processing devicemay communicate the at least one prediction and/or the treatment data tothe computing device of the healthcare provider.

At 1012, the processing device may receive, from the computing device ofthe healthcare provider, treatment plan input indicating at least onemodification to the at least one of the at least one aspect of thetreatment plan and any other aspect of the treatment plan.

At 1014, the processing device may modify, using the treatment planinput, the at least one of the at least one aspect of the treatment planand any other aspect of the treatment plan.

At 1016, the processing device may control, during a telemedicinesession while the user uses the treatment device 70 and based on themodified at least one of the at least one aspect of the treatment planor any other aspect of the treatment plan, the treatment device 70.

FIG. 11 is a flow diagram generally illustrating an alternative method1100 for monitoring, based on treatment data received while a user usesthe treatment device 70, characteristics of the user while the user usesthe treatment device 70 according to the principles of the presentdisclosure. Method 1100 includes operations performed by processors of acomputing device (e.g., any component of FIG. 1, such as server 30executing the artificial intelligence engine 11). In some embodiments,one or more operations of the method 1100 are implemented in computerinstructions stored on a memory device and executed by a processingdevice. The method 1100 may be performed in the same or a similar manneras described above in regard to method 900 and/or method 1000. Theoperations of the method 1100 may be performed in some combination withany of the operations of any of the methods described herein.

At 1102, the processing device may receive treatment data pertaining toa user who uses a treatment device, such as the treatment device 70, toperform a treatment plan. The treatment data may include characteristicsof the user, baseline measurement information pertaining to the user,measurement information pertaining to the user while the user uses thetreatment device 70 to perform the treatment plan, characteristics ofthe treatment device 70, at least one aspect of the treatment plan,other suitable data, or a combination thereof.

At 1104, the processing device may write to an associated memory, foraccess by an artificial intelligence engine, such as the artificialintelligence engine 11, the treatment data. The artificial intelligenceengine 11 may be configured to use at least one machine learning model,such as the machine learning model 13. The machine learning model 13 maybe configured to use at least one aspect of the treatment data togenerate at least one prediction.

The at least one prediction may indicate one or more predictedcharacteristics of the user. The one or more predicted characteristicsof the user may include a predicted vital sign of the user, a predictedrespiration rate of the user, a predicted heartrate of the user, apredicted temperature of the user, a predicted blood pressure of theuser, a predicted outcome of the treatment plan being performed by theuser, a predicted injury of the user resulting from the user performingthe treatment plan, or other suitable predicted characteristic of theuser.

At 1106, the processing device may receive, from the artificialintelligence engine 11, the at least one prediction.

At 1108, the processing device may generate treatment information usingthe at least one prediction. The treatment information may include asummary of the performance, while the user uses the treatment device 70to perform the treatment plan, of the treatment plan by the user and theat least one prediction. The treatment information may be formatted,such that the treatment data and/or the at least one prediction ispresentable at a computing device of a healthcare provider responsiblefor the performance of the treatment plan by the user.

At 1110, the processing device may write, to an associated memory foraccess by at least one of the computing device of the healthcareprovider and a machine learning model executed by the artificialintelligence engine 11, the treatment information and/or the at leastone prediction.

At 1112, the processing device may receive treatment plan inputresponsive to the treatment information. The treatment plan input mayindicate at least one modification to the at least one aspect treatmentplan and/or any other aspect of the treatment plan. In some embodiments,the treatment plan input may be provided by the healthcare provider, asdescribed. In some embodiments, based on the treatment information, theartificial intelligence engine 11 executing the machine learning model13 may generate the treatment plan input.

At 1114, the processing device may determine whether the treatment planinput indicates at least one modification to the at least one aspecttreatment plan and/or any other aspect of the treatment plan.

If the processing device determines that the treatment plan input doesnot indicate at least one modification to the at least one aspecttreatment plan and/or any other aspect of the treatment plan, theprocessing device returns to 1102 and continues receiving treatment datapertaining to the user while the user uses the treatment device 70 toperform the treatment plan. If the processing device determines that thetreatment plan input indicates at least one modification to the at leastone aspect treatment plan and/or any other aspect of the treatment plan,the processing device continues at 1116.

At 1116, the processing device may selectively modify the at least oneaspect of the treatment plan and/or any other aspect of the treatmentplan. For example, the processing device may determine whether thetreatment data indicates that the treatment plan is having a desiredeffect. The processing device may modify, in response to determiningthat the treatment plan is not having the desired effect, at least oneaspect of the treatment plan in order to attempt to achieve the desiredeffect, and if not possible, at least a portion of the desired effect.

At 1118, the processing device may control, while the user uses thetreatment device 70, based on treatment plan and/or the modifiedtreatment plan the treatment device 70.

FIG. 12 generally illustrates an example embodiment of a method 1200 forreceiving a selection of an optimal treatment plan and controlling atreatment device while the patient uses the treatment device accordingto the present disclosure, based on the optimal treatment plan. Method1200 includes operations performed by processors of a computing device(e.g., any component of FIG. 1, such as server 30 executing theartificial intelligence engine 11). In some embodiments, one or moreoperations of the method 1200 are implemented in computer instructionsstored on a memory device and executed by a processing device. Themethod 1200 may be performed in the same or a similar manner asdescribed above in regard to method 900. The operations of the method1200 may be performed in some combination with any of the operations ofany of the methods described herein.

Prior to the method 1200 being executed, various optimal treatment plansmay be generated by one or more trained machine learning models 13 ofthe artificial intelligence engine 11. For example, based on a set oftreatment plans pertaining to a medical condition of a patient, the oneor more trained machine learning models 13 may generate the optimaltreatment plans. The various treatment plans may be transmitted to oneor more computing devices of a patient and/or medical professional.

At 1202 of the method 1200, the processing device may receive an optimaltreatment plan selected from some or all of the optimal treatment plans.The selection may have been entered on a user interface presenting theoptimal treatment plans on the patient interface 50 and/or the assistantinterface 94.

At 1204, the processing device may control, while the patient uses thetreatment device 70, based on the selected optimal treatment plan, thetreatment device 70. In some embodiments, the controlling may beperformed distally by the server 30. For example, if the selection ismade using the patient interface 50, one or more control signals may betransmitted from the patient interface 50 to the treatment device 70 toconfigure, according to the selected treatment plan, a setting of thetreatment device 70 to control operation of the treatment device 70.Further, if the selection is made using the assistant interface 94, oneor more control signals may be transmitted from the assistant interface94 to the treatment device 70 to configure, according to the selectedtreatment plan, a setting of the treatment device 70 to controloperation of the treatment device 70.

It should be noted that, as the patient uses the treatment device 70,the sensors 76 may transmit measurement data to a processing device. Theprocessing device may dynamically control, according to the treatmentplan, the treatment device 70 by modifying, based on the sensormeasurements, a setting of the treatment device 70. For example, if theforce measured by the sensor 76 indicates the user is not applyingenough force to a pedal 102, the treatment plan may indicate to reducethe required amount of force for an exercise.

It should be noted that, as the patient uses the treatment device 70,the user may use the patient interface 50 to enter input pertaining to apain level experienced by the patient as the patient performs thetreatment plan. For example, the user may enter a high degree of painwhile pedaling with the pedals 102 set to a certain range of motion onthe treatment device 70. The pain level entered by the user may bewithin a range or at a level which may cause the range of motion to bedynamically adjusted based on the treatment plan. For example, thetreatment plan may specify alternative range of motion settings if acertain pain level is indicated when the user is performing an exercisesubject to a certain range of motion.

FIG. 13 generally illustrates an example computer system 1300 which canperform any one or more of the methods described herein, in accordancewith one or more aspects of the present disclosure. In one example,computer system 1300 may include a computing device and correspond tothe assistance interface 94, reporting interface 92, supervisoryinterface 90, clinician interface 20, server 30 (including the AI engine11), patient interface 50, ambulatory sensor 82, goniometer 84,treatment device 70, pressure sensor 86, or any suitable component ofFIG. 1. The computer system 1300 may be capable of executinginstructions implementing the one or more machine learning models 13 ofthe artificial intelligence engine 11 of FIG. 1. The computer system maybe connected (e.g., networked) to other computer systems in a LAN, anintranet, an extranet, or the Internet, including via the cloud or apeer-to-peer network.

The computer system may operate in the capacity of a server in aclient-server network environment. The computer system may be a personalcomputer (PC), a tablet computer, a wearable (e.g., wristband), aset-top box (STB), a personal Digital Assistant (PDA), a mobile phone, acamera, a video camera, an Internet of Things (IoT) device, or anydevice capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that device. Further,while only a single computer system is illustrated, the term “computer”shall also be taken to include any collection of computers thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methods discussed herein.

The computer system 1300 includes a processing device 1302, a mainmemory 1304 (e.g., read-only memory (ROM), flash memory, solid statedrives (SSDs), dynamic random access memory (DRAM) such as synchronousDRAM (SDRAM)), a static memory 1306 (e.g., flash memory, solid statedrives (SSDs), static random access memory (SRAM)), and a data storagedevice 1308, which communicate with each other via a bus 1310.

Processing device 1302 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 1302 may be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processor implementing other instruction sets orprocessors implementing a combination of instruction sets. Theprocessing device 1402 may also be one or more special-purposeprocessing devices such as an application specific integrated circuit(ASIC), a system on a chip, a field programmable gate array (FPGA), adigital signal processor (DSP), network processor, or the like. Theprocessing device 1402 is configured to execute instructions forperforming any of the operations and steps discussed herein.

The computer system 1300 may further include a network interface device1312. The computer system 1300 also may include a video display 1314(e.g., a liquid crystal display (LCD), a light-emitting diode (LED), anorganic light-emitting diode (OLED), a quantum LED, a cathode ray tube(CRT), a shadow mask CRT, an aperture grille CRT, a monochrome CRT), oneor more input devices 1316 (e.g., a keyboard and/or a mouse or agaming-like control), and one or more speakers 1318 (e.g., a speaker).In one illustrative example, the video display 1314 and the inputdevice(s) 1316 may be combined into a single component or device (e.g.,an LCD touch screen).

The data storage device 1316 may include a computer-readable medium 1320on which the instructions 1322 embodying any one or more of the methods,operations, or functions described herein is stored. The instructions1322 may also reside, completely or at least partially, within the mainmemory 1304 and/or within the processing device 1302 during executionthereof by the computer system 1300. As such, the main memory 1304 andthe processing device 1302 also constitute computer-readable media. Theinstructions 1322 may further be transmitted or received over a networkvia the network interface device 1312.

While the computer-readable storage medium 1320 is generally illustratedin the illustrative examples to be a single medium, the term“computer-readable storage medium” should be taken to include a singlemedium or multiple media (e.g., a centralized or distributed database,and/or associated caches and servers) that store the one or more sets ofinstructions. The term “computer-readable storage medium” shall also betaken to include any medium that is capable of storing, encoding orcarrying a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thepresent disclosure. The term “computer-readable storage medium” shallaccordingly be taken to include, but not be limited to, solid-statememories, optical media, and magnetic media.

Clause 1. A computer-implemented system, comprising: a treatment deviceconfigured to be manipulated by a user while the user is performing atreatment plan; a patient interface comprising an output deviceconfigured to present telemedicine information associated with atelemedicine session; and a first computing device configured to:receive treatment data pertaining to the user while the user uses thetreatment device to perform the treatment plan, wherein the treatmentdata comprises at least one of characteristics of the user, baselinemeasurement information pertaining to the user, measurement informationpertaining to the user while the user performs the treatment plan,characteristics of the treatment device, and at least one aspect of thetreatment plan; write to an associated memory, for access by anartificial intelligence engine, the treatment data, the artificialintelligence engine being configured to use at least one machinelearning model, wherein the machine learning model uses at least oneaspect of the treatment data to generate at least one prediction;receive, from the artificial intelligence engine, the at least oneprediction; identify a threshold corresponding to the at least oneprediction; in response to a determination that the at least oneprediction is within a range of the threshold, provide via an interface,at a second computing device of a healthcare provider, the at least oneprediction and the treatment data; and, in response to a determinationthat the at least one prediction is outside of the range of thethreshold, update the treatment data pertaining to the user to indicatethe at least one prediction.

Clause 2. The computer-implemented system of any clause herein, whereinthe first computing device is further configured to receive, from theinterface of the second computing device of the healthcare provider,treatment plan input, wherein the treatment plan input includes at leastone modification to the treatment plan.

Clause 3. The computer-implemented system of any clause herein, whereinthe first computing device is further configured to modify the treatmentplan using the at least one modification indicated in the treatment planinput.

Clause 4. A method comprising: receiving treatment data pertaining to auser who uses a treatment device to perform a treatment plan, whereinthe treatment data comprises at least one of characteristics of theuser, baseline measurement information pertaining to the user,measurement information pertaining to the user while the user performsthe treatment plan, characteristics of the treatment device, and atleast one aspect of the treatment plan; writing to an associated memory,for access by an artificial intelligence engine, the treatment data, theartificial intelligence engine being configured to use at least onemachine learning model that uses at least one aspect of the treatmentdata to generate at least one prediction; receiving, from the artificialintelligence engine, the at least one prediction; identifying athreshold corresponding to the at least one prediction; in response to adetermination that the at least one prediction is within a range of thethreshold, communicating with an interface, at a computing device of ahealthcare provider, the at least one prediction and the treatment data;and, in response to a determination that the at least one prediction isoutside of the range of the threshold, updating the treatment datapertaining to the user to indicate the at least one prediction.

Clause 5. The method of any clause herein, further comprising receiving,from the interface of the computing device of the healthcare provider,treatment plan input, wherein the treatment plan input includes at leastone modification to the treatment plan.

Clause 6. The method of any clause herein, further comprising modifyingthe treatment plan using the at least one modification indicated in thetreatment plan input.

Clause 7. The method of any clause herein, further comprisingcontrolling, while the user uses the treatment device during atelemedicine session and based on the modified treatment plan, thetreatment device.

Clause 8. The method of any clause herein, wherein at least some of thetreatment data corresponds to sensor data from a sensor associated withthe treatment device.

Clause 9. The method of any clause herein, wherein at least some of thetreatment data corresponds to sensor data from a sensor associated withthe user while using the treatment device.

Clause 10. The method of any clause herein, wherein the sensorassociated with the user is comprised of a wearable device worn by theuser.

Clause 11. The method of any clause herein, wherein the baselinemeasurement information includes, while the user is at rest, at leastone of a vital sign of the user, a respiration rate of the user, aheartrate of the user, a temperature of the user, a blood pressure ofthe user, a blood oxygen saturation level of the user, a blood glucoselevel of the user, the eye dilation level of the user, a biomarker levelof the user, and wherein the measurement information includes, while theuser performs the treatment plan, at least one of a vital sign of theuser, a respiration rate of the user, a heartrate of the user, atemperature of the user, a blood pressure of the user, a blood oxygensaturation level of the user, a blood glucose level of the user, the eyedilation level of the user, and a biomarker level of the user.

Clause 12. The method of any clause herein, wherein the at least onemachine learning model includes a deep network comprising more than onelevel of non-linear operations.

Clause 13. A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to: receivetreatment data pertaining to a user who uses a treatment device toperform a treatment plan, wherein the treatment data comprises at leastone of characteristics of the user, baseline measurement informationpertaining to the user, measurement information pertaining to the userwhile the user performs the treatment plan, characteristics of thetreatment device, and at least one aspect of the treatment plan; writeto an associated memory, for access by an artificial intelligenceengine, the treatment data, the artificial intelligence engine beingconfigured to use at least one machine learning model that uses at leastone aspect of the treatment data to generate at least one prediction;receive, from the artificial intelligence engine, the at least oneprediction; identify a threshold corresponding to the at least oneprediction; in response to a determination that the at least oneprediction is within a range of the threshold, communicate with aninterface, at a computing device of a healthcare provider, to providethe at least one prediction and the treatment data; and, in response toa determination that the at least one prediction is outside of the rangeof the threshold, update the treatment data pertaining to the user toindicate the at least one prediction.

Clause 14. The computer-readable medium of any clause herein, whereinthe instructions further cause the processing device to receive, fromthe interface of the computing device of the healthcare provider,treatment plan input, wherein the treatment plan input includes at leastone modification to the treatment plan.

Clause 15. The computer-readable medium v, wherein the instructionsfurther cause the processing device to modify the treatment plan usingthe at least one modification indicated in the treatment plan input.

Clause 16. The computer-readable medium of any clause herein, whereinthe instructions further cause the processing device to control, whilethe user uses the treatment device during a telemedicine session andbased on the modified treatment plan, the treatment device.

Clause 17. The computer-readable medium of any clause herein, wherein atleast some of the treatment data corresponds to sensor data from asensor associated with the treatment device.

Clause 18. The computer-readable medium of any clause herein, wherein atleast some of the treatment data corresponds to sensor data from asensor associated with the user while using the treatment device.

Clause 19. The computer-readable medium of any clause herein, whereinthe sensor associated with the user is comprised of a wearable deviceworn by the user.

Clause 20. The computer-readable medium of any clause herein, whereinthe baseline measurement information includes, while the user is atrest, at least one of a vital sign of the user, a respiration rate ofthe user, a heartrate of the user, a temperature of the user, a bloodpressure of the user, a blood oxygen saturation level of the user, ablood glucose level of the user, the eye dilation level of the user, anda biomarker level of the user, and wherein the measurement informationincludes, while the user performs the treatment plan, at least one of avital sign of the user, a respiration rate of the user, a heartrate ofthe user, a temperature of the user, a blood pressure of the user, ablood oxygen saturation level of the user, a blood glucose level of theuser, the eye dilation level of the user, and a biomarker level of theuser.

Clause 21. The computer-readable medium of any clause herein, whereinthe at least one machine learning model includes a deep networkcomprising more than one level of non-linear operations.

Clause 22. A system comprising: a processing device; and a memoryincluding instructions that, when executed by the processor, cause theprocessor to: receive treatment data pertaining to a user who uses atreatment device to perform a treatment plan, wherein the treatment datacomprises at least one of characteristics of the user, baselinemeasurement information pertaining to the user, measurement informationpertaining to the user while the user performs the treatment plan,characteristics of the treatment device, and at least one aspect of thetreatment plan; write to an associated memory, for access by anartificial intelligence engine, the treatment data, the artificialintelligence engine being configured to use at least one machinelearning model that uses at least one aspect of the treatment data togenerate at least one prediction; receive, from the artificialintelligence engine, the at least one prediction; identify a thresholdcorresponding to the at least one prediction; in response to adetermination that the at least one prediction is within a range of thethreshold, communicate with an interface, at a computing device of ahealthcare provider, to provide the at least one prediction and thetreatment data; and, in response to a determination that the at leastone prediction is outside of the range of the threshold, update thetreatment data pertaining to the user to indicate the at least oneprediction.

Clause 23. The system of any clause herein, wherein the instructionsfurther cause the processing device to receive, from the interface ofthe computing device of the healthcare provider, treatment plan input,wherein the treatment plan input includes at least one modification tothe treatment plan.

Clause 24. The system of any clause herein, wherein the instructionsfurther cause the processing device to modify the treatment plan usingthe at least one modification indicated in the treatment plan input.

Clause 25. The system of any clause herein, wherein the instructionsfurther cause the processing device to control, while the user uses thetreatment device during a telemedicine session and based on the modifiedtreatment plan, the treatment device.

Clause 26. The system of any clause herein, wherein at least some of thetreatment data corresponds to sensor data from a sensor associated withthe treatment device.

Clause 27. The system of any clause herein, wherein at least some of thetreatment data corresponds to sensor data from a sensor associated withthe user while using the treatment device.

Clause 28. The system of any clause herein, wherein the sensorassociated with the user is comprised of a wearable device worn by theuser.

Clause 29. The system of any clause herein, wherein the baselinemeasurement information includes, while the user is at rest, at leastone of a vital sign of the user, a respiration rate of the user, aheartrate of the user, a temperature of the user, a blood pressure ofthe user, a blood oxygen saturation level of the user, a blood glucoselevel of the user, the eye dilation level of the user, and a biomarkerlevel of the user, and wherein the measurement information includes,while the user performs the treatment plan, at least one of a vital signof the user, a respiration rate of the user, a heartrate of the user, atemperature of the user, a blood pressure of the user, a blood oxygensaturation level of the user, a blood glucose level of the user, the eyedilation level of the user, and a biomarker level of the user.

Clause 30. The system of any clause herein, wherein the at least onemachine learning model includes a deep network comprising more than onelevel of non-linear operations.

The above discussion is meant to be illustrative of the principles andvarious embodiments of the present disclosure. Numerous variations andmodifications will become apparent to those skilled in the art once theabove disclosure is fully appreciated. It is intended that the followingclauses be interpreted to embrace all such variations and modifications.

The various aspects, embodiments, implementations, or features of thedescribed embodiments can be used separately or in any combination. Theembodiments disclosed herein are modular in nature and can be used inconjunction with or coupled to other embodiments.

Consistent with the above disclosure, the examples of assembliesenumerated in the following clauses are specifically contemplated andare intended as a non-limiting set of examples.

What is claimed is:
 1. A computer-implemented system, comprising: atreatment device configured to be manipulated by a user while the useris performing a treatment plan; a patient interface comprising an outputdevice configured to present telemedicine information associated with atelemedicine session; and a first computing device configured to:receive treatment data pertaining to the user while the user uses thetreatment device to perform the treatment plan, wherein the treatmentdata comprises at least one of characteristics of the user, baselinemeasurement information pertaining to the user, measurement informationpertaining to the user while the user performs the treatment plan,characteristics of the treatment device, and at least one aspect of thetreatment plan; write to an associated memory, for access by anartificial intelligence engine, the treatment data, the artificialintelligence engine being configured to use at least one machinelearning model, wherein the machine learning model uses at least oneaspect of the treatment data to generate at least one prediction;receive, from the artificial intelligence engine, the at least oneprediction; identify a threshold corresponding to the at least oneprediction; in response to a determination that the at least oneprediction is within a range of the threshold, provide via an interface,at a second computing device of a healthcare provider, the at least oneprediction and the treatment data; and in response to a determinationthat the at least one prediction is outside of the range of thethreshold, update the treatment data pertaining to the user to indicatethe at least one prediction.
 2. The computer-implemented system of claim1, wherein the first computing device is further configured to receive,from the interface of the second computing device of the healthcareprovider, treatment plan input, wherein the treatment plan inputincludes at least one modification to the treatment plan.
 3. Thecomputer-implemented system of claim 2, wherein the first computingdevice is further configured to modify the treatment plan using the atleast one modification indicated in the treatment plan input.
 4. Amethod comprising: receiving treatment data pertaining to a user whouses a treatment device to perform a treatment plan, wherein thetreatment data comprises at least one of characteristics of the user,baseline measurement information pertaining to the user, measurementinformation pertaining to the user while the user performs the treatmentplan, characteristics of the treatment device, and at least one aspectof the treatment plan; writing to an associated memory, for access by anartificial intelligence engine, the treatment data, the artificialintelligence engine being configured to use at least one machinelearning model that uses at least one aspect of the treatment data togenerate at least one prediction; receiving, from the artificialintelligence engine, the at least one prediction; identifying athreshold corresponding to the at least one prediction; in response to adetermination that the at least one prediction is within a range of thethreshold, communicating with an interface, at a computing device of ahealthcare provider, the at least one prediction and the treatment data;and in response to a determination that the at least one prediction isoutside of the range of the threshold, updating the treatment datapertaining to the user to indicate the at least one prediction.
 5. Themethod of claim 4, further comprising receiving, from the interface ofthe computing device of the healthcare provider, treatment plan input,wherein the treatment plan input includes at least one modification tothe treatment plan.
 6. The method of claim 5, further comprisingmodifying the treatment plan using the at least one modificationindicated in the treatment plan input.
 7. The method of claim 6, furthercomprising controlling, while the user uses the treatment device duringa telemedicine session and based on the modified treatment plan, thetreatment device.
 8. The method of claim 4, wherein at least some of thetreatment data corresponds to sensor data from a sensor associated withthe treatment device.
 9. The method of claim 4, wherein at least some ofthe treatment data corresponds to sensor data from a sensor associatedwith the user while using the treatment device.
 10. The method of claim9, wherein the sensor associated with the user is comprised of awearable device worn by the user.
 11. The method of claim 4, wherein thebaseline measurement information includes, while the user is at rest, atleast one of a vital sign of the user, a respiration rate of the user, aheartrate of the user, a temperature of the user, a blood pressure ofthe user, a blood oxygen saturation level of the user, a blood glucoselevel of the user, the eye dilation level of the user, a biomarker levelof the user, and wherein the measurement information includes, while theuser performs the treatment plan, at least one of a vital sign of theuser, a respiration rate of the user, a heartrate of the user, atemperature of the user, a blood pressure of the user, a blood oxygensaturation level of the user, a blood glucose level of the user, the eyedilation level of the user, and a biomarker level of the user.
 12. Themethod of claim 4, wherein the at least one machine learning modelincludes a deep network comprising more than one level of non-linearoperations.
 13. A tangible, non-transitory computer-readable mediumstoring instructions that, when executed, cause a processing device to:receive treatment data pertaining to a user who uses a treatment deviceto perform a treatment plan, wherein the treatment data comprises atleast one of characteristics of the user, baseline measurementinformation pertaining to the user, measurement information pertainingto the user while the user performs the treatment plan, characteristicsof the treatment device, and at least one aspect of the treatment plan;write to an associated memory, for access by an artificial intelligenceengine, the treatment data, the artificial intelligence engine beingconfigured to use at least one machine learning model that uses at leastone aspect of the treatment data to generate at least one prediction;receive, from the artificial intelligence engine, the at least oneprediction; identify a threshold corresponding to the at least oneprediction; in response to a determination that the at least oneprediction is within a range of the threshold, communicate with aninterface, at a computing device of a healthcare provider, to providethe at least one prediction and the treatment data; and in response to adetermination that the at least one prediction is outside of the rangeof the threshold, update the treatment data pertaining to the user toindicate the at least one prediction.
 14. The computer-readable mediumof claim 13, wherein the instructions further cause the processingdevice to receive, from the interface of the computing device of thehealthcare provider, treatment plan input, wherein the treatment planinput includes at least one modification to the treatment plan.
 15. Thecomputer-readable medium of claim 14, wherein the instructions furthercause the processing device to modify the treatment plan using the atleast one modification indicated in the treatment plan input.
 16. Thecomputer-readable medium of claim 15, wherein the instructions furthercause the processing device to control, while the user uses thetreatment device during a telemedicine session and based on the modifiedtreatment plan, the treatment device.
 17. The computer-readable mediumof claim 13, wherein at least some of the treatment data corresponds tosensor data from a sensor associated with the treatment device.
 18. Thecomputer-readable medium of claim 13, wherein at least some of thetreatment data corresponds to sensor data from a sensor associated withthe user while using the treatment device.
 19. The computer-readablemedium of claim 18, wherein the sensor associated with the user iscomprised of a wearable device worn by the user.
 20. Thecomputer-readable medium of claim 13, wherein the baseline measurementinformation includes, while the user is at rest, at least one of a vitalsign of the user, a respiration rate of the user, a heartrate of theuser, a temperature of the user, a blood pressure of the user, a bloodoxygen saturation level of the user, a blood glucose level of the user,the eye dilation level of the user, and a biomarker level of the user,and wherein the measurement information includes, while the userperforms the treatment plan, at least one of a vital sign of the user, arespiration rate of the user, a heartrate of the user, a temperature ofthe user, a blood pressure of the user, a blood oxygen saturation levelof the user, a blood glucose level of the user, the eye dilation levelof the user, and a biomarker level of the user.
 21. Thecomputer-readable medium of claim 13, wherein the at least one machinelearning model includes a deep network comprising more than one level ofnon-linear operations.
 22. A system comprising: a processing device; anda memory including instructions that, when executed by the processor,cause the processor to: receive treatment data pertaining to a user whouses a treatment device to perform a treatment plan, wherein thetreatment data comprises at least one of characteristics of the user,baseline measurement information pertaining to the user, measurementinformation pertaining to the user while the user performs the treatmentplan, characteristics of the treatment device, and at least one aspectof the treatment plan; write to an associated memory, for access by anartificial intelligence engine, the treatment data, the artificialintelligence engine being configured to use at least one machinelearning model that uses at least one aspect of the treatment data togenerate at least one prediction; receive, from the artificialintelligence engine, the at least one prediction; identify a thresholdcorresponding to the at least one prediction; in response to adetermination that the at least one prediction is within a range of thethreshold, communicate with an interface, at a computing device of ahealthcare provider, to provide the at least one prediction and thetreatment data; and in response to a determination that the at least oneprediction is outside of the range of the threshold, update thetreatment data pertaining to the user to indicate the at least oneprediction.
 23. The system of claim 22, wherein the instructions furthercause the processing device to receive, from the interface of thecomputing device of the healthcare provider, treatment plan input,wherein the treatment plan input includes at least one modification tothe treatment plan.
 24. The system of claim 23, wherein the instructionsfurther cause the processing device to modify the treatment plan usingthe at least one modification indicated in the treatment plan input. 25.The system of claim 24, wherein the instructions further cause theprocessing device to control, while the user uses the treatment deviceduring a telemedicine session and based on the modified treatment plan,the treatment device.
 26. The system of claim 22, wherein at least someof the treatment data corresponds to sensor data from a sensorassociated with the treatment device.
 27. The system of claim 22,wherein at least some of the treatment data corresponds to sensor datafrom a sensor associated with the user while using the treatment device.28. The system of claim 27, wherein the sensor associated with the useris comprised of a wearable device worn by the user.
 29. The system ofclaim 22, wherein the baseline measurement information includes, whilethe user is at rest, at least one of a vital sign of the user, arespiration rate of the user, a heartrate of the user, a temperature ofthe user, a blood pressure of the user, a blood oxygen saturation levelof the user, a blood glucose level of the user, the eye dilation levelof the user, and a biomarker level of the user, and wherein themeasurement information includes, while the user performs the treatmentplan, at least one of a vital sign of the user, a respiration rate ofthe user, a heartrate of the user, a temperature of the user, a bloodpressure of the user, a blood oxygen saturation level of the user, ablood glucose level of the user, the eye dilation level of the user, anda biomarker level of the user.
 30. The system of claim 22, wherein theat least one machine learning model includes a deep network comprisingmore than one level of non-linear operations.